Department of
COMPUTER SCIENCE






Syllabus for
Bachelor of Science (Computer Science, Mathematics, Statistics)
Academic Year  (2023)

 
3 Semester - 2022 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
AEN321 ADDITIONAL ENGLISH 3 3 100
CSC331 DATABASE MANAGEMENT SYSTEM AND SOFTWARE ENGINEERING 4 4 100
CSC351 PYTHON PROGRAMMING LAB 2 2 50
ENG321 ENGLISH-III 3 2 100
FRN321 FRENCH 3 3 100
HIN321 HINDI 3 3 100
KAN321 KANNADA 3 03 50
MAT331 REAL ANALYSIS 4 4 100
MAT351 PYTHON PROGRAMMING FOR MATHEMATICS 2 2 50
SAN321 SANSKRIT 3 3 100
STA331 STATISTICAL INFERENCE 4 4 100
STA351 STATISTICAL INFERENCE PRACTICAL 2 2 50
STA371 APPLIED EXCEL 4 4 100
TAM321 TAMIL 3 3 100
4 Semester - 2022 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
AEN421 ADDITIONAL ENGLISH 3 3 100
CSC431 COMPUTER NETWORKS AND JAVA PROGRAMMING 4 4 100
CSC451 JAVA PROGRAMMING LAB 2 2 50
ENG421 ENGLISH-IV 3 2 100
FRN421 FRENCH 3 3 100
HIN421 HINDI 3 3 100
KAN421 KANNADA 3 03 50
MAT431 ALGEBRA 4 4 100
MAT451 PYTHON PROGRAMMING FOR MATHEMATICAL MODELLING 2 2 50
SAN421 SANSKRIT 3 3 100
STA431 ELEMENTS OF STOCHASTIC PROCESS 4 4 100
STA451 ELEMENTS OF STOCHASTIC PROCESS PRACTICAL 2 2 50
TAM421 TAMIL 3 3 100
5 Semester - 2021 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
CSC541A DATA ANALYTICS 3 3 100
CSC541B INTERNET OF THINGS 3 3 100
CSC541C DIGITAL IMAGE PROCESSING 2 2 50
CSC541D BUSINESS INTELLIGENCE 3 3 100
CSC542A UNIX OPERATING SYSTEM 3 3 100
CSC542B WEB TECHNOLOGY 3 3 100
CSC542C MOBILE APPLICATIONS 3 3 100
CSC542D GRAPHICS AND ANIMATION 3 3 100
CSC542E .NET TECHNOLOGY 3 3 100
CSC551A DATA ANALYTICS LAB 2 2 50
CSC551B INTERNET OF THINGS LAB 3 3 100
CSC551C DIGITAL IMAGE PROCESSING LAB 2 2 50
CSC551D BUSINESS INTELLIGENCE LAB 2 2 50
CSC552A UNIX OPERATING SYSTEM LAB 3 3 100
CSC552B WEB TECHNOLOGY LAB 2 2 50
CSC552C MOBILE APPLICATIONS LAB 2 2 50
CSC552D GRAPHICS AND ANIMATION LAB 2 2 50
CSC552E .NET TECHNOLOGY LAB 2 2 50
MAT531 LINEAR ALGEBRA 3 3 100
MAT541A INTEGRAL TRANSFORMS 3 3 100
MAT541B MATHEMATICAL MODELLING 3 3 100
MAT541C GRAPH THEORY 3 3 100
MAT541D CALCULUS OF SEVERAL VARIABLES 3 3 100
MAT541E OPERATIONS RESEARCH 3 3 100
MAT551 LINEAR ALGEBRA USING PYTHON 2 2 50
MAT551A INTEGRAL TRANSFORMS USING PYTHON 2 2 50
MAT551B MATHEMATICAL MODELLING USING PYTHON 2 2 50
MAT551C GRAPH THEORY USING PYTHON 2 2 50
MAT551D CALCULUS OF SEVERAL VARIABLES USING PYTHON 2 2 50
MAT551E OPERATIONS RESEARCH USING PYTHON 2 2 50
STA531 LINEAR REGRESSION MODELS 3 3 100
STA541A SAMPLING TECHNIQUES 3 3 100
STA541B DESIGN OF EXPERIMENTS 3 3 100
STA541C ACTUARIAL STATISTICS 3 3 100
STA541D INTRODUCTION TO SPATIAL STATISTICS 3 3 100
STA551 LINEAR REGRESSION MODELS PRACTICAL 2 2 50
STA552A SAMPLING TECHNIQUES PRACTICAL 2 2 50
STA552B DESIGN OF EXPERIMENTS PRACTICAL 2 2 50
STA552C ACTUARIAL STATISTICS PRACTICAL 2 2 50
STA552D SPATIAL STATISTICS PRACTICAL 2 2 50
6 Semester - 2021 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
CSC631 DESIGN AND ANALYSIS OF ALGORITHMS 3 3 100
CSC641A INTRODUCTION TO SOFT COMPUTING 3 3 100
CSC641B CLOUD COMPUTING 3 3 100
CSC641C COMPUTER ARCHITECTURE 3 3 100
CSC641D OOAD USING UML 4 4 100
CSC641E USER EXPERIENCE DESIGN(UX) 3 3 100
CSC681 MAIN PROJECT 4 4 100
MAT631 COMPLEX ANALYSIS 3 3 100
MAT641A MECHANICS 3 3 100
MAT641B NUMERICAL METHODS 3 3 100
MAT641C DISCRETE MATHEMATICS 3 3 100
MAT641D NUMBER THEORY 3 3 100
MAT641E FINANCIAL MATHEMATICS 3 3 100
MAT651 COMPLEX ANALYSIS USING PYTHON 2 2 50
MAT651A MECHANICS USING PYTHON 2 2 50
MAT651B NUMERICAL METHODS USING PYTHON 2 2 50
MAT651C DISCRETE MATHEMATICS USING PYTHON 2 2 50
MAT651D NUMBER THEORY USING PYTHON 2 2 50
MAT651E FINANCIAL MATHEMATICS USING EXCEL AND PYTHON 2 2 50
MAT681 PROJECT ON MATHEMATICAL MODELS 5 5 150
STA631 TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES 3 3 100
STA641A APPLIED STATISTICS 3 3 100
STA641B STATISTICAL QUALITY CONTROL 3 3 100
STA641C BIOSTATISTICS 3 3 100
STA641D STATISTICAL GENETICS 3 3 100
STA651 TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES PRACTICAL 2 2 50
STA652A APPLIED STATISTICS PRACTICAL 2 2 50
STA652B STATISTICAL QUALITY CONTROL PRACTICAL 2 2 50
STA652C BIOSTATISTICS PRACTICAL 2 2 50
STA652D STATISTICAL GENETICS PRACTICAL 2 2 50

AEN321 - ADDITIONAL ENGLISH (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course Description

 

This course is taught in the second year for students from different streams, namely BA, BSc

 

and BCom. If the first year syllabus is an attempt by the Department of English, Christ

 

University to recognize and bring together the polyphonic Indian voices in English and Indian

 

regional literatures in translation for the Additional English students of the first year, the

 

second year syllabus intends to take that project a little further and open up the engagement

 

of the students to texts from across the world. The syllabus - selection of texts will

 

concentrate on readings from South Asian, Latin American, Australian, Canadian, and Afro-

 

American. It will voice subaltern concerns of identity, gender, race, ethnicity and problems of

 

belongingness experienced by humanity all over the globe.

 

The syllabus will extend the concerns of nation and nationality and marginalization,

 

discussed within the Indian context to a more inclusive and wider global platform. We have

 

consciously kept out ‘mainstream’ writers and concentrated on the voices of the subalterns

 

from across the world. There is an implicit recognition in this project that though the aspects

 

of marginalization and the problems facing subalterns are present across cultures and

 

nations, the experiences, expressions and reflections are specific to each race and culture.

 

The course will address these nuances and specificities and enable our students to become

 

more aware and sensitive to life and reality around them. This will equip the students, who

 

are global citizens, to understand not just the Indian scenario, but also situate themselves

 

within the wider global contexts and understand the spaces they will move into and negotiate

 

in their future.

 

There is a prescribed text book Blends: Voices from Margins for the second year students,

 

compiled by the Department of English, Christ University and intended for private circulation.

Course Objectives

 

The course objectives are

 

 to enable students to look at different cultures through Literature

 

 to help students develop an understanding of subaltern realities and identity politics

 

 to inculcate literary sensibility/taste among students across disciplines

 

 to improve language skills –speaking, reading, writing and listening

 

 to equip the students with tools for developing lateral thinking

 

 to equip students with critical reading and thinking habits

 

 to reiterate the study skills and communication skills they developed in the previous

 

year and extend it.

Learning Outcome

CO1: it will enable students to understand and analyse the nuances of cultures, ethnicities and other diversity around them and become sensitive towards them.

CO2 : They will be able to critique literature from a cultural, ethical, social and political perspectives

Unit-1
Teaching Hours:12
Children?s Novel
 

TetsukoKuroyanagi: Tottochan: The Little Girl at the Window12

Unit-2
Teaching Hours:12
Short Story
 

Liliana Heker : “The Stolen Party

 

 Higuchi Ichiyo: “Separate Ways”

 

 Harukki Murakami "Birthday Girl"

 

 Luisa Valenzuela: “I’m your Horse in the Night”

 

Unit-3
Teaching Hours:12
Poetry
 

Poetry 12 Hrs

 

 Silvio Curbelo: “Summer Storm”

 

 Nancy Morejon: “Black Woman”

 

 Ruben Dario: “To Roosevelt”

 

 Mina Asadi: “A Ring to me is a Bondage”

Unit-4
Teaching Hours:9
Essay
 

Essay 9Hrs

 

 Amy Tan: “Mother Tongue

 

 Linda Hogan: “Waking Up the Rake”

 

 Isabelle Allande: “Open Veins of Latin America”

Text Books And Reference Books:

Blends Book II

Essential Reading / Recommended Reading

Oxford Encyclopeadia on Latin American History

Children's Literature -  Kimberley Reynolds (CUP)

Evaluation Pattern

Evaluation Pattern

 

CIA 1: A written test for 20 marks. It can be an Open Book test, a classroom assignment, an

 

objective or descriptive test pertaining to the texts and ideas discussed in class.

 

CIA2: Mid-semester written exam for 50 works

 

CIA 3: This is to be a creative test/ project in small groups by students. They may do

 

Collages, tableaus, skits, talk shows, documentaries, Quizzes, presentations, debates,

 

charts or any other creative test for 20 marks. This test should allow the students to explore

 

their creativity and engage with the real world around them and marks can be allotted to

 

students depending on how much they are able to link the ideas and discussions in the texts

 

to the world around them.

 

Question Paper Pattern

 

Mid Semester Exam: 2 hrs

 

Section A: 4x5= 20

 

Section B: 2x15=30

 

Total 50

 

End Semester Exam: 3 hrs

 

Section A: 4 x 5 = 20

 

Section B: 2 x 15= 30

 

Total 50

CSC331 - DATABASE MANAGEMENT SYSTEM AND SOFTWARE ENGINEERING (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course enables the students to apply the concepts of database management system and helps to understand the concept of software engineering principles. This course also focuses on the important steps in designing the software project.

Learning Outcome

CO1: Understand the basic concepts of relational database models and software engineering.

CO2: Demonstrate database operations using Relational Calculus and Algebra.

CO3: Design normalized database applications.

CO4: Analyze and find the practical solutions to the problem by applying the software process.

Unit-1
Teaching Hours:5
DATABASES AND DATABASE USERS
 

Data- Database- Database management system- Characteristics of the  database  approach-  Role of Database administrators- Role of Database Designers- End Users- Advantages  of Using a DBMS and When not to use a DBMS-Database System Concepts and Architecture- Data Models- Categories of data models- Schemas- Instances- and  Database  states-  The  Three schema architecture- Data independence- DBMS Languages and Interfaces- Classification of Database Management Systems.

Unit-2
Teaching Hours:8
BASIC SQL
 

SQL data definition and data types- specifying constraints in SQL- SQL functions- Basic queries-Filtering data using where- Group by statements- DDL- DML- Retrieving data from multiple tables- Sub queries- Concept of a view in SQL.

Unit-3
Teaching Hours:5
THE RELATIONAL ALGEBRA AND RELATIONAL CALCULUS
 

Relational Algebra: Unary relational operations; Binary relational operations ; Examples of queries in relational algebra, Relational calculus: The Tuple relational calculus; The Domain relational calculus.

Unit-4
Teaching Hours:5
DATA MODELING USING ENTITY_RELATIONSHIP MODEL
 

Using  High   Level   Conceptual   Data   Models   for   Database   Design-    Example  Database applications-Entity types- Entity Sets-Attributes and Keys- Relationships- Relationship types- Roles and Structural constraints- Weak Entity Types- Drawing E- R Diagrams.

Unit-5
Teaching Hours:7
DATABASE DESIGN
 

Functional dependencies and Normalization for Relational Databases-  Normalization  concepts- Normal forms-1NF- 2NF- 3NF- BCNF- 4NF.

Unit-6
Teaching Hours:5
SOFTWARE AND SOFTWARE ENGINEERING
 

Nature of software - Defining software, Software Application Domains, Legacy Software - Software Engineering, The software process, Software Engineering practice - The essence of Practice, General Principles - Software Crisis and Myths.

Unit-7
Teaching Hours:6
PROCESS MODELS
 

A generic process model – Defining a framework activity, identifying a Task Set, Process Patterns - Process Assessment and improvement, Prescriptive Process Models – The waterfall Model, Incremental Model, Evolutionary Process Model, Concurrent Models - A Final Word on Evolutionary Processes

Unit-8
Teaching Hours:6
UNDERSTANDING REQUIREMENTS
 

Requirements Engineering, Establishing the groundwork – Identifying Stakeholders, Recognizing multiple viewpoints, Working toward Collaboration, Asking the first questions - Eliciting requirements - Collaborative requirement gathering, Quality function Deployment, Usage Scenario Elicitation Work Products - Developing use cases, building the requirements model – Elements of the requirements Model, Analysis pattern - Negotiating requirements, validating requirements.

Unit-9
Teaching Hours:6
DESIGN CONCEPTS
 

The design within the context of Software Engineering, The design process – Software quality guidelines and attributes, The evolution of software design - Design concepts – Abstraction, Architecture, Patterns, Separation of concerns, Modularity, information hiding, Functional Independence, refinement, Aspects, Refactoring, Object Oriented design concepts Design classes. The design Model – Data Design elements, Architectural Design elements, Interface Design Elements, Component - Level Design elements, Deployment level Design elements.

Unit-10
Teaching Hours:7
Software Testing
 

A Strategic approach to testing- Verification and Validation, Organizing for software testing, software testing strategy, Criteria for completion of testing-Test strategies for conventional software - Unit testing, Integration testing- Test strategies for Object Oriented software-Unit testing in the OO Context, Integration testing in the OO Context - Validation testing, White- box testing, Basic path testing- Flow Graph Notation, Independent program paths, Deriving test cases, Graph matrices- control structure testing – Condition testing, Data flow testing, loop testing- Black-box testing-Graph-based testing methods, Equivalence partitioning, boundary value analysis.

Text Books And Reference Books:

[1] Fundamentals of Database Systems, Shamkanth B Navathe, Ramez Elmasri, 7th Edition, Pearson Education, 2017.

[2] Pressman S Roger, Software Engineering A Practitioner’s Approach, McGraw Hill International Editions, 7th edition, 2010.

Essential Reading / Recommended Reading

[1] Database System Concepts, Abraham Silberschatz, Henry F Korth, S Sudarshan, McGraw Hill Education, 6th edition, 2017.

[2] Sommerville, Ian, Software Engineering, Addison Wesley, 9th Edition, 2010.

[3] Pankaj Jalote, Software Engineering: A Precise Approach, Wiley India, 2010.

[4] Stephen R. Schach, Software Engineering, Tata McGraw-Hill Publishing Company Limited, 2007.

Evaluation Pattern

CIA: 50%

ESE: 50%

CSC351 - PYTHON PROGRAMMING LAB (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course covers the programming paradigms associated with python. It explores the object-oriented programming, Graphical programming aspects of python with help of built-in modules.

Learning Outcome

CO1: Understand the use of built-in objects of Python.

CO2: Demonstrate significant experience with python program development environment.

CO3: Develop GUI programming concepts.

Unit-1
Teaching Hours:4
PYTHON DATA STRUCTURES
 

Sequences, Mapping and Sets- Dictionaries- Functions - Lists and Mutability – String Operations.

Program 1

Demonstrate the use of lists, sets, tuples, dictionaries and perform the string operations.

Unit-2
Teaching Hours:4
MODULES AND OBJECT ORIENTED PROGRAMMING USING PYTHON
 

Mechanism of Module Execution - Problem Solving Using Lists and Functions - Classes: Classes and Instances-Inheritance.

Program 2

Perform the usage and creation of custom modules and demonstrate the features of OOP concepts using Python.

Unit-3
Teaching Hours:4
EXCEPTION HANDLING AND REGULAR EXPRESSION
 

Exception Handling: Catching and Raising Exceptions, Custom Exceptions – Regular Expression: Character Classes, Quantifiers, Grouping and Capturing, Assertions and Flags.

Program 3

Demonstrate the usage of Exception Handling and Perform Regular Expression operations for different scenario.

Unit-4
Teaching Hours:4
GUI PROGRAMMING - ROOT WINDOW
 

GUI Programming Introduction-Tkiner module-Root window-Widgets-Button-Label.

Program 4

Create a system application using Tkinter and perform the usage of different widgets.

Unit-5
Teaching Hours:4
GUI PROGRAMMING - WIDGETS AND TABLES
 

Tkinter Message types – Text – Menu - Listboxes – Spinbox - Creating tables.

Program 5

 

Perform the operation using list and populate the values in the form of tables inside GUI using Tkinter.

Unit-6
Teaching Hours:4
WEB FRAMEWORK - DJANGO
 

Introduction-Web framework-creating model to add database service - Django administration application.

Program 6

Develop a web application using Django and explain the procedure of creating model and accessing model using Django administration application.

Unit-7
Teaching Hours:3
Numpy
 

Computation on NumPy-Aggregations-Computation on Arrays-Comparisons, Masks and Boolean Arrays-Sorting Arrays.

Program 7

Demonstrate Indexing and Sorting using Numpy.

Unit-8
Teaching Hours:3
Pandas
 

Pandas Objects-Data indexing and Selection-Operating on Data in Pandas-Handling Missing Data.

Program 8

Demonstrate handling of missing data using Pandas.

Text Books And Reference Books:

[1] Wesely J.Chun,Core Python Application Programming ,Prentice Hall,third edition 2015.

[2]T.R.Padmanabhan, Programming with Python,Springer Publications,2016.

Essential Reading / Recommended Reading

[1] Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications, 2016.

Evaluation Pattern

CIA – 50 %

ESE - 50 %

ENG321 - ENGLISH-III (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:2

Course Objectives/Course Description

 

 

Course Description

English is offered as a course for all the students in BA, BSc, BCom, and BBA F&A classes in the third and fourth semesters. The aim is to strengthen the communication skills, and particularly study skills of the learners further, through adequate practice and exposure to good examples of writing, thought, ideas and human values. In addition, they will be trained in study skills through tasks in academic genres such as message, letter, essay, data interpretation etc. It aims to not only equip learners with skills but also sensitize them towards issues that concern human life in today’s globalised context. The course content is selected to meet the requirements of the departmental goal of “empowering the individual to read oneself, the social context and the imagined”; institutional goal of ensuring “holistic development”; and the national goal of creating competent and valuable citizens. The primary objective of this course is to help learners develop appropriate employability skills and demonstrate suitable conduct with regards to communication skills. The units are organised in order to help the learners understand the academic and workplace demands and learn by practice.

 

Course Objectives     

 

 

·       To enable learners to develop reading comprehension for various purposes

 

·       To enable learners to develop writing skills for academic and professional needs

 

·       To enable learners to develop the ability to think critically and express logically

 

·       To enable learner to communicate in a socially and ethically acceptable manner

 

·       To enable learners, to read, write and speak with clarity, precision and accuracy

 

 

 

 

 

 

 

 

 

 

 

 

 

Learning Outcome

CO1: Recognise the errors of usage and correct them. Recognize their own ability to improve their own competence in using the language

CO2: Read independently unfamiliar texts with comprehension. Read longer texts, compare, and evaluate them.

CO3: Understand the importance of writing in academic life. Write simple sentences without committing errors in spelling and grammar. Plan a piece of writing using drafting techniques.

Unit-1
Teaching Hours:10
Introduction to university grammar
 

 

Subject verb agreement

 

Tenses

 

Preposition

 

Voices

 

Clauses

 

Unit-2
Teaching Hours:10
Strategies for Reading
 

 

Skimming and scanning

 

Strategies of reading

 

Reading and understanding reports

 

Reading content/ texts of various kinds

 

Inferencing skills

 

Academic vocab

 

Academic phrases

 

Professional expression

 

Study skills- library and referencing skills (organising reading, making notes, managing time, prioritising)

 

Unit-3
Teaching Hours:10
Strategic writing for academic purpose
 

 

Mind mapping

 

Organising ideas

 

Accurate usage of vocabulary

 

Paragraph strategy

 

Cohesion and sequencing (jumbled sentences to paragraph)

 

Extended writing 

 

Formal and informal writing

 

Reports (all types including illustration to report and report to illustration and/or graphs, charts, tables and other statistical data)

 

Proposal writing (for projects, for research)

 

Academic essays/ articles

 

Persuasive writing, extrapolative writings

 

Case study writing

 

Executive summaries

 

Editing, proofreading skills

 

Resume vs CV

 

Unit-4
Teaching Hours:10
Listening and Oral communication
 

 

Self-introduction

 

Body language

 

Talks, speeches and presentations

 

Conversation

 

Telephone conversation

 

Meetings

 

Group discussion

 

Seminar / conference presentation

 

Unit-5
Teaching Hours:5
Business communication
 

 

Principles of communication

 

Process of communication

 

Types of communication

Barriers in communication

Text Books And Reference Books:

NIL

Essential Reading / Recommended Reading

ENGlogue -2

Evaluation Pattern

 

Evaluation Pattern

 

CIA 1: Classroom assignment/test/ written or oral tasks for 20 marks keeping in tune with the course objectives and learning outcomes.

CIA 2: Mid-semester exam for 50 marks.

CIA 3: Collage, tableaus, skits, talk shows, documentaries, Quizzes or any creative assignments.

 

 End- semester 50 marks 

 

End Semester Exam: 2 hrs

 

 

 

 

 

 

 

 

 

 

 

 

FRN321 - FRENCH (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

French as second language for the Arts, Science and Commerce UG program

Learning Outcome

CO1: Ability to communicate with native speakers and make presentations on small topics

CO 2: Proficiency in literary analysis, appreciation and review of poems,play ,films and fables

CO3: Acquaintance of culture, civilization, social values and etiquettes, and gastronomical richness

CO 4: Ability to do formal and informal, oral and written communication.

CO 5: Overall knowledge on functional and communicative aspects and get through a2 level exams.

Unit-1
Teaching Hours:9
Dossier 1
 

To perform a tribute: artist, work, you are going to…..

Unit-2
Teaching Hours:9
Dossier 2
 

Towards a working life

Unit-3
Teaching Hours:9
Dossier 3
 

France Seen by...

Unit-4
Teaching Hours:9
Dossier 4
 

Mediamania

Unit-5
Teaching Hours:9
Le Bourgeois Gentilhomme
 

Act 1, 2 & 3

Text Books And Reference Books:

1.        Berthet, Annie, Catherine Hugot et al. Alter Ego + A2. Paris : Hachette, 2012

2.      Gonnet, Georges. Molière- Le Bourgeois Gentilhomme .Paris : Hachette, 1971

Essential Reading / Recommended Reading

1.      Lichet, Raymond., Puig Rosado. Ecrire à tout le monde. Paris : Hachette, 1980

2.      French websites like Bonjour de France, FluentU French, Learn French Lab, Point du FLE etc.

Evaluation Pattern

Assessment Pattern

CIA (Weight)

ESE (Weight)

CIA 1 – Assignments / Letter writing / Film review

10%

 

CIA 2 –Mid Sem Exam

25%

 

CIA 3 – Quiz / Role Play / Theatre / Creative projects 

10%

 

Attendance

05%

 

End Sem Exam

 

50%

Total

50%

50%

HIN321 - HINDI (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

Course Description:

The detailed text book “Shambook” is a Khanda Kavya written by Jagdeesh Gupta. To improve the creative writing skills, Nibandh, Kahani and Kavitha lekhan are included.Bharathiya chitrakala is also a part of the syllabus to improve the knowledge aboutIndian paintings.

Course Objectives:

Students are exposed to different forms of poetry especially, Khanda Kavya. It will help them to understand the contemporary socio-political issues.By learning about the tradition of Indian painting and legendary painters of India , students get to know about the richness and culture  of the Indian paintings. Creative writing sharpens their thinking, analytical  and writing skills 

Learning Outcome

CO1: By the end of the course the student should be able to: ● CO1: Improve their writing skill in literary Hindi by doing asynchronous session assignments and CIAs. ● CO2: Improve their analytical skills through critical analysis of the poetry. ● CO3: Will be able to learn the different aspects of Official correspondence. ● CO4: To improve their basic research skills while doing the CIAs. By the end of the course the student should be able to: ● CO1: Improve their writing skill in literary Hindi by doing assignments and CIAs

CO2: Improve their analytical skills through critical analysis of the poetry.

CO3: To improve their basic research skills while doing the CIAs

CO4: To understand the contributions of painters to Indian painting.

Unit-1
Teaching Hours:15
Shambooh
 

Khanda Kavya “Shambook” [Poetry] By:Jagdeesh Gupta. Pub: Raj Pal & Sons

 

Level of knowledge:Analitical    

Unit-2
Teaching Hours:15
Creative writing
 

Nibandh lekhan, Katha lekhan, Kavitha lekhan.

Level of knowledge:Conceptual

Unit-3
Teaching Hours:15
Bharathiya chithrakala -parampara evam pramukh kalakar
 

Utbhav, vikas aur pramukh shailiyam

pramukh kalakar-1.M F Hussain 2.Ravindranath Tagore 3.Raja Ravi Varma 4.Jamini Roy.

Level of knowledge: Conceptual

Text Books And Reference Books:

  1. Khanda Kavya”Shambook[Poetry] ByJagdeesh Gupta.Pub: Raj Pal & Sons
Essential Reading / Recommended Reading

.1. Sugam Hindi Vyakaran – Prof. Vamsidhar and Dharampal Shastry, SikshaBharathi,New Delh

2. Essentials of Screen writing: The art, craft and business of film and television writing

By: Walter Richard.

3. Writing and Script: A very short introduction

By: Robinson, Andrew.

4 .Creative writing By John Singleton

5. Adhunik  Hindi Nibandh By Bhuvaneshwarichandran Saksena.

Evaluation Pattern

CIA-1(Digital learning-wikipedia)

CIA-2(Mid sem examination)

CIA-3(wikipedia article creation)

End semester examination

KAN321 - KANNADA (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:50
Credits:03

Course Objectives/Course Description

 

Course Description: Language Kannada is offered to students of third Semester BA/B.Sc as Second language for fifty marks. Students of this semester will study an anthology of Modern Kannada Poetry and an Autobiography of Laxman Gaikwad. This course prepares the students to understand the new era. At the dawn of the twentieth century, B.M. Srikantiah, regarded as the “Father of modern Kannada Literature”, called for a new era of writing original works in modern Kannada while moving away from archaic Kannada forms. Students will study modern Kannada poetry from B.M.Sri to Dalit poet Dr. Siddalingiah. An anthology of modern poetry is selected to understand the beauty of modern Kannada poets through their writings. Uchalya is an autobiographical novel that carries the memories of Laxman Gaikwad right from his childhood till he became an adult. Laxman Gaikwad took birth in a criminal tribe of India belonging to Orissa/ Maharastra. The original text is translated to Kannada by Chandrakantha Pokale.

 

Course Objectives:

Understand and appreciate poetry as a literary art form.

Analyse the various elements of Poetry, such as diction, tone, form, genre, imagery, symbolism, theme, etc.

Appreciates to  learn the elements of autobiography.

Learning Outcome

CO 1: Able to define autobiography

CO2: Outline a personal autobiography

CO3: Delineate different types of autobiography

CO 4: Proficiency in communication skills

CO5 : Understand the principles of translation

Unit-1
Teaching Hours:15
Modern Kannada Poetry
 

1. Kariheggadeya Magalu- B.M.Sri

2. Hunnime Ratri- Kuvempu

3. Anna Yagna-Bendre

4.Mankuthimmana Kagga-D.V.G

5.Ikkala- K.S. Narasimha Swamy

6. Kannad padgol- G.P.Rajarathnam

7.Hanathe hachchuttene- G.S.S

8.Adugemane Hudugi-Vaidehi

9. Nehru Nivruttaraguvudilla- Adgaru

10. Nanna Janagalu.-Siddalingaiah

Unit-2
Teaching Hours:20
Autobiography- Uchalya- Lakshman Gayekwad (Marathi)
 

Text: Uchalya

Author:Lakshman Gayekwad

Translation: Chandrakantha Pokle

 

Unit-3
Teaching Hours:10
Creative Writings
 

 

1 Dialogue Writing

2 Essay writing

3 short story building

Text Books And Reference Books:

1. English Geethegalu- Sri, Publishers: B.M.Sri Smarka Prathistana, Bangalore-19 (2013)

2. Kannada Sahitya Charithre- Volumes 1-4, Editor: G. S. Shivarudrappa, Prasaranga, Bangalore Univeristy.

3. Hosagannada Kavitheya Mele English Kavyada Prabhava- S. Ananthanarayana

4. Hosagannadada Arunodaya- Srinivasa  Havanuru

Essential Reading / Recommended Reading

1. Hosagannda Sahitya- L.S. Sheshagiri Rao

2. Kannada Sahitya Sameekshe- G. S. Shivarudrappa

3. Bhavageethe- Dr. S. Prabhushankara

4. My Experiments with Truth- M.K. Gandhi

5. Ouru Keri- Siddalingaiah

Evaluation Pattern
 
Evaluation Pattern
 

CIA-1 Wikipedia Assignments- 20 Marks

CIA-2 Mid Semsester Examination- 50 Marks

CIA-3 Wikipedia Assignment-20 Marks

Attendance -10 Marks

End Semester Examination- 50 Marks

 
   

MAT331 - REAL ANALYSIS (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course description : This course enables the students to understand the basic techniques and theories of real Analysis.

 

Course objectives : This course will help the learner to

COBJ1. examine the convergence or divergence of sequences and series.

COBJ2. understand the different types of convergence and their properties.

 

Learning Outcome

Course outcomes : On successful completion of the course, the students should be able to

CO1. Quote and understand the definition of a limit of a sequence or a function in its various forms.

CO2. Demonstrate the convergence or divergence of the geometric and harmonic series and other standard series.

CO3. Apply the basic tests for convergence of infinite series.

CO4. Prove the tests for convergence: Comparison Test, Ratio Test, Cauchy’s Root test, Raabe’s Test, alternating series test etc.

CO5. Understand the differences between convergence and absolute convergence

CO6. Understand and solve binomial , logarithmic and exponential series

Unit-1
Teaching Hours:20
Sets and Sequences
 

Open sets, closed sets, closure of a set, countable and uncountable sets, topology of real line. Sequences: Definition of Sequences, limit of a sequence, algebra of limits of a sequence, convergent, divergent, and oscillatory sequences, problems thereon. Bounded sequences, Monotonic sequences and their properties, Cauchy sequence.

Unit-2
Teaching Hours:20
Infinite Series
 

Infinite series, Cauchy convergence criterion for series, geometric series, comparison test, convergence of p-series, D'Alembert's Ratio test, Raabe's test, Cauchy's Root test, alternating series, Leibnitz’s test. Definition and examples of absolute and conditional convergence.

Unit-3
Teaching Hours:20
Sequence and Series of functions
 

Sequences and series of functions, Pointwise and uniform convergence. Mn - test, M-test, Statements of the results about uniform convergence. Power series and radius of convergence.

Text Books And Reference Books:

S.C.Malik and Savita Arora, Mathematical Analysis , Second Edition, New Delhi, India: New Age international (P) Ltd., 2005.

Essential Reading / Recommended Reading
  1. R.G. Bartle and D. R Sherbert, Introduction to Real Analysis, John Wiley and Sons (Asia) P. Ltd., 2000.
  2. E. Fischer, Intermediate Real Analysis ,1 st ed.(Reprint), Springer Verlag, 2012.
  3. K.A. Ross, Elementary Analysis- The Theory of Calculus Series- Undergraduate Texts in Mathematics, Springer Verlag, 2003.
  4. S Narayana and M.D. Raisinghania, Elements of Real Analysis, Revised ed., S. Chand & Company Ltd, 2011.
  5. T. M. Apostol, Calculus (Vol. I), John Wiley and Sons (Asia) P. Ltd., 2002.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ,

Written Assignment,

Reference work, etc.,

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT351 - PYTHON PROGRAMMING FOR MATHEMATICS (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: The course Python programming for Mathematics is aimed at enabling the students to appreciate and understand some concepts in mathematics like Matrices, sequences, series, geometric shapes and fractals with the help of Python programming language. It is designed with a learner-centric approach wherein the students will acquire mastery in the subject by using Python programing language as tool.

Course objectives: This course will help the learner to

COBJ1. Acquire programming skill in solving mathematical problems using Python

Learning Outcome

CO1: demonstrate the use of Python to understand and interpret the concepts in sequences and series.

CO2: apply Python to finding the area of the curve.

CO3: acquire proficiency in using Python to find out the inverse determinant, transpose, Eigen values of a Matrix.

CO4: visualize shapes and Fractals

Unit-1
Teaching Hours:30
Proposed Topics
 
  1.  Introduction to NumPy and Sympy
  2. Algebra and Symbolic Math with SymPy
  3. Matrices - determinant, transpose, lower and upper triangular matrices, Eigen values
  4. Solving linear and nonlinear equations
  5. Test for Convergence of Sequences
  6. Test for Convergence of Series
  7. Drawing Geometric Shapes and Fractals
  8. Complex functions in Python
Text Books And Reference Books:
  1. H. Brian, A Practical Introduction to Python Programming, Creative Commons Attribution, 2012.
  2. A. Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, No Starch Press, 2015.
Essential Reading / Recommended Reading

H. P. Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer, 2016.

Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

SAN321 - SANSKRIT (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Sundara Kanda is the only chapter of the Ramayana in which the hero is not Rama, but rather Hanuman. The work depicts the adventures of Hanuman and his selflessness, strength, and devotion to Rama are emphasized in the text. Bhoja only wrote 5 kāṇdas (up to the Sundarakāṇda), and there is a story about this: that he was inspired to write this work the night before a battle, that as he finished the Sundarakāṇda it was time to go, and that he announced that the Yuddhakāṇda would be enacted in the battlefield against the invader, but sadly he never returned. Others have composed a Yuddhakāṇda to complete the work.

The main objective of the students is to understand the champu Kavyas based on the sam.  

The Origin and development of the Champu.

Learning Outcome

CO1: To analyse the content of the text in detail with examples

CO2: To Deliberate the classification and characters of the epic

CO3: To understand the delight of the text.

CO4: To demonstrate an increased ability to read and understand Sanskrit texts

CO5: To understand the prefixes and suffixes and changing the sentences in grammar.

Unit-1
Teaching Hours:35
champu
 

Origin and developmetn of Champu kavyas

Five Important Champus

Level of knowledge: Basic/conceptual/ Analytical

Shlokas 1 -60 Hnumantha¨s voyage to Lanka and searching for Seetha Description of city Lanka , Characters of Champu Kavya 

Unit-2
Teaching Hours:5
Grammar
 

Prayogas and Krudantha

Unit-3
Teaching Hours:5
Language skills
 

Translation of Given passage from English to Sanskrit 

Writing composition in sanskrit on the given topic in Sanskrit

Text Books And Reference Books:

Sundarakanda from Bhaja´s Champu Ramayana 

Chitrakalayaa: ugagamam vikaasam ca

origin and development of painting through Vedas and Puranas

 

Essential Reading / Recommended Reading

   

Reference Books:-

 

1)      Sundarakanda from “Champuramayana of Bhoja  

2)      Sanskrit Grammar by M.R. Kale.

3)       History of Sanskrit literature by Dr.M.S. Shivakumaraswamy.

4)       History of Sanskrit literature by Krishnamachari.

 

 

Evaluation Pattern

CIA 1 Wikipedia assignment

CIA 2 mid semester examination

CIA 3 Wikipedia assignment

STA331 - STATISTICAL INFERENCE (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course is designed to introduce the concepts of theory of estimation and testing of hypothesis. This paper also deals with the concept of parametric tests for large and small samples. It also provides knowledge about non-parametric tests and its applications.

Learning Outcome

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples.

CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses.

CO3: Apply the concept of nonparametric tests for single sample and two samples.

Unit-1
Teaching Hours:10
Introduction
 

 

Concept of Population – Sample - Sample Space - Parameter and Statistic - Parameter Space - Sampling distribution of a statistic - Standard error - Derivation of Standard Error of sample mean – variance - proportion and difference between variances - Concept of Order Statistics.

Unit-2
Teaching Hours:15
Theory of Estimation
 

Point Estimation: Concept of Estimator and Estimate - properties of Point estimator - Unbiasedness - Consistency - efficiency - relative efficiency - Minimum variance unbiased estimators - sufficiency - Crammer Rao Inequality (Statement only) - Rao Blackwell Theorem (Statement only) - Neyman Factorization Theorem (Statement only) - Methods of Estimation: Maximum likelihood - least squares and minimum variance - Concept of Interval Estimation.

Unit-3
Teaching Hours:10
Tests of Significance I
 

 

Concept of Statistical hypotheses - Type I and Type II error - Critical Region and power of the test - Neyman-Pearson lemma (Statement only) - Large sample tests: Tests for single mean - equality of two means - single variance and equality of two variance for normal population - Tests of proportions.

Unit-4
Teaching Hours:15
Tests of Significance II
 

 

Sampling distributions of Chi-square - t and F statistics: derivation of Mean - variance - M.G.F and properties (without proof) - Small sample tests: Tests for single mean - equality of two means - single variance and equality of two variance - Tests of proportions based on t and F statistics - Chi-square tests for independence of attributes and goodness of fit.

Unit-5
Teaching Hours:10
Nonparametric Tests
 

Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann-Whitney-Wilcoxon tests for two samples

Text Books And Reference Books:

 

  1. Rohatgi V.K., Statistical Inference, Dover Publication, New York, 2013.

  2. Gupta S.C. and Kapoor V.K., Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020.

Essential Reading / Recommended Reading
  1. Walpole R.E, Myers R.H and Myers S.L, Probability and Statistics for Engineers and Scientists, 9th edition, Pearson, New Delhi, 2017.

  2. John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014.

  3. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012.

Rohatgi V.K an Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc, New Jersey, 2015

Evaluation Pattern

50% CIA and 50% ESE

STA351 - STATISTICAL INFERENCE PRACTICAL (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is designed to give a practical exposure for testing of hypothesis by analyzing various data sets using R programming.

Learning Outcome

CO1: Demonstrate the parametric tests for small and large samples using R programming.

CO2: Demonstrate the non-parametric tests for real time data using R programming.

Unit-1
Teaching Hours:30
Practical Assignments using R programming:
 
  1. Test for mean and equality of two means when variance is known under normality conditions.
  2. Test for single mean when variance is unknown under normality conditions.
  3. Test for equality of two means when variance is unknown under normality conditions.
  4. Test for single proportion
  5. Test for equality of two proportions.
  6. Test for variance and equality of variances under normality conditions.
  7. Test for independence of attributes using Chi-Square test.
  8. Test for goodness fit using Chi-Square test.
  9. Test for one sample using Run test and sign test.
  10. Test for paired samples using Wilcoxon Signed Rank test
  11. Test for two samples using Run test and Median test
  12. Test for two samples using Mann-Whitney-Wilcoxon test.
Text Books And Reference Books:

1.Rohatgi V.K., Statistical Inference, Dover Publication, New York, 2013.

Essential Reading / Recommended Reading

 

  1. Walpole R.E, Myers R.H and Myers S.L, Probability and Statistics for Engineers and Scientists, 9th edition, Pearson, New Delhi, 2017.
  2. John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014.
  3. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012.
  4. Rohatgi V.K an Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc, New Jersey, 2015.
Evaluation Pattern

ESE-50%

CIA-50%

STA371 - APPLIED EXCEL (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course description: This course is designed to build the logical thinking ability and to provide hands-on experience in solving statistical models using MS Excel with Problem based learning. To explore and visualize data using excel formulas and data analysis tool pack.

 

Learning Outcome

CO1: Demonstrate the logics of using excel features.

CO2: Demonstrate the building blocks of excel, excel shortcuts, sample data creation and analyzing data.

CO3: Analyze the data sets using Data Analysis Pack.

Unit-1
Teaching Hours:10
Basics
 

Introduction: File types - Spreadsheet structure - Menu bar - Quick access toolbar - Mini toolbar

- Excel options - Formatting: Format painter - Font - Alignment - Number - Styles - Cells, Clear

 

- Page layout - Symbols - Equation - Editing - Link - Filter - Charts - Formula Auditing - Overview of Excel tables and properties - Collecting sample data and arranging in definite format in Excel tables

Unit-2
Teaching Hours:10
File exchange and Data cleaning
 

Importing data from different sources - text file - web page and XML file - Exporting data in different formats - text - csv - image -pdf etc - Creating database with the imported data - Data tools: text to column - identifying and removing duplicates - using format cell options - Application of functions - Concatenate - Upper - Lower - Trim - Repeat - Proper - Clean - Substitute - Convert - Left - Right - Mid - Len - Find - Exact - Replace - Text join - Value - Fixed etc.

Unit-3
Teaching Hours:15
Handling missing data and Excel functions
 

Data manipulation in table using shortcuts - using formulas and function - Missing value handling in graph using example of scatter graph with connecting line - Logical functions: AND

- OR - XOR - NOT - Conditional functions: IF - IFERROR - IFS - SWITCH - Date and Time: Date - Time - Now - Today - Year - Eomonth - Edate - Workdays - Workdays.Intl - Yearfrac - Lookup and Reference Functions: LOOKUP - VLOOKUP - HLOOKUP - INDEX - MATCH.CHOOSE - OFFSET - HYPERLINK - Mathematical Operations: SUM - PRODUCT - AGGREGATE - SUBTOTAL - Statistical Functions: Count - Frequency - Percentiles - Quartiles

 

- Rank - Deviation - Variance - Averages etc

Unit-4
Teaching Hours:15
Data analysis
 

Data analysis tool pack: measures of central tendency - dispersion - skewness - kurtosis - partition values - graphical and diagrammatic representation of data: histogram - bar diagram - charts - line graphs - Ogive - covariance - correlation - linear regression

Unit-5
Teaching Hours:10
Macros and Security
 

 

Introduction to macros - using macros for data entry - importing files - Data cleaning and managing using macro - Different types of security available in Excel to protect the contents. Construction of dashboard.

Text Books And Reference Books:

 

  1. Alexander R, Kuselika R and Walkenbach J, Microsoft Excel 2019 Bible, Wiley India Pvt Ltd, New Delhi, 2018.

  2. Paul M, Microsoft Excel 2019 formulas and functions, Pearson Eduction, 2019.

Essential Reading / Recommended Reading

 

  1. Olafusi M, Microsoft Excel and Business Data Analysis for the Busy Professional, Create Space Independent Publishing Platform, 2016.

  2. McFedries P, Excel Data Analysis Visual Blueprint, 4th Edition, Wiley India Pvt Ltd, New Delhi, 2013.

  3. www.excelfunctions.net

  4. www.excel-easy.com

Evaluation Pattern

CIA 50%

ESE 50%

TAM321 - TAMIL (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Araillakiyam, bakthi illakiyam, ikala illakiyamn the major allakiyams.The influence myths and puranas are delineated through the good deeds for a better lifestyle.The  Cultural Studies part will have an overview of Indian painting both traditional and modern with special reference to mythology and literature

India 2020- Abdul Kalam

 

 

Learning Outcome

CO1: Recall and categorize the concepts of literature.

CO2: Understand the true essence of the texts, and inculcate them in their daily lives.

CO3: Recognize and apply the moral values and ethics in their learning.

CO4: Comprehend the concepts in literature and appreciate the literary text.

Unit-1
Teaching Hours:10
Ara illakiyam
 

1. Thirukural

2. Avvai kural

Unit-2
Teaching Hours:10
Bhakthi illakiyam
 

1. Thiru vasagam

2. Kambar andhadhi

 

Unit-3
Teaching Hours:10
Ik kaala illakiyam
 

Naatu pura padalgal

Unit-4
Teaching Hours:10
Prose
 

India 2020- Dr. Abdul Kalam

Unit-5
Teaching Hours:3
Common Topic and visual text
 

1. Common topic: Oviyam

2. Visual text : nattupuviyal

Unit-6
Teaching Hours:2
Grammer
 

Sollu illakanam

Text Books And Reference Books:

Thirukkural-Bhoombugar pathipagam- puliyur kesigan urai, Chennai- 08

Kammbarin Ainthu noolgal- Vanathi pathupagam- Dr. R. Rajagopalachariyar,  Chennai- 18

Nathu pura illakiyam- Ki Va jaganathan- malai aruvi- Monarch achagam- chennai

India 2020- APJ Abdul kalam- puthaiyuram aandugaluku aga oru thoali nooku,  New century book house, chennai

 

 

Essential Reading / Recommended Reading

 

Thirukkural-Bhoombugar pathipagam- puliyur kesigan urai, Chennai- 08

Kammbarin Ainthu noolgal- Vanathi pathupagam- Dr. R. Rajagopalachariyar,  Chennai- 18

Nathu pura illakiyam- Ki Va jaganathan- malai aruvi- Monarch achagam- chennai

India 2020- APJ Abdul kalam- puthaiyuram aandugaluku aga oru thoali nooku,  New century book house, chennai

Tamizhar nattup padagal - N Vanamamalai, New century book house, Chennai

 

 

 

 

Evaluation Pattern

EXAMINATION AND ASSIGNMENTS: There is a continuous evaluation both at the formal and informal levels. The language skills and the ability to evaluate a text will be assessed

This paper will have a total of 50 marks shared equally by End Semester Exam (ESE) and Continuous Internal Assessment (CIA) While the ESE is based on theory the CIA will assess the students' critical thinking, leadership qualities, language skills and creativity



AEN421 - ADDITIONAL ENGLISH (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is taught in the second year for students from different streams, namely BA, BSc and B Com. If the first year syllabus is an attempt by the Department of English, Christ University to recognize and bring together the polyphonic Indian voices in English and Indian regional literatures in translation for the Additional English students of the first year, the second year syllabus intends to take that project a little further and open up the engagement of the students to texts from across the world. The syllabus - selection of texts will concentrate on readings from South Asian, Latin American, Australian, Canadian, and Afro-American. It will voice subaltern concerns of identity, gender, race, ethnicity and problems of belongingness experienced by humanity all over the globe.

The syllabus will extend the concerns of nation and nationality and marginalization, discussed within the Indian context to a more inclusive and wider global platform. We have consciously kept out ‘mainstream’ writers and concentrated on the voices of the subalterns from across the world. There is an implicit recognition in this project that though the aspects of marginalization and the problems facing subalterns are present across cultures and nations, the experiences, expressions and reflections are specific to each race and culture. The course will address these nuances and specificities and enable our students to become more aware and sensitive to life and reality around them. This will equip the students, who are global citizens, to understand not just the Indian scenario, but also situate themselves within the wider global contexts and understand the spaces they will move into and negotiate in their future.

 

There is a prescribed text book Blends: Voices from Margins for the second year students, compiled by the Department of English, Christ University and intended for private circulation. 

The course objectives are

·         to introduce the students to look at different cultures through Literature

·         to help students develop an understanding of subaltern realities and identity politics

·         to inculcate literary sensibility/taste among students across disciplines

·         to improve language skills –speaking, reading, writing and listening

·         to equip the students with tools for developing lateral thinking

·         to equip students with critical reading and thinking habits

·         to enable them to grasp and appreciate the variety and abundance of subaltern writing, of which this compilation is just a glimpse 

·         to actively engage with the world as a cultural and social space (to be facilitated through proactive CIAs which help students to interact and engage with the realities they face everyday and have come across in these texts)

·         to learn and appreciate India and its place in the world through association of ideas in the texts and the external contexts

 

·         to reiterate the study skills and communication skills they developed in the previous year and extend it.  

Learning Outcome

CO1 : CO1: To understand the socio- political concerns in various literatures through short stories, poems and essays

CO2: CO2: To critically read and articulate the non- canonised literatures

CO3: CO3: To analyse and apply these textual themes in a multi- cultural, global and professional space

Unit-1
Teaching Hours:12
Novella
 

Unit 1: Novella

·         Viktor Frankl: “Man’s Search for Meaning”(Excerpts)                                       

 

 

Unit-2
Teaching Hours:12
Short Stories
 

Short Story                                                                                                    

·         Anton Chekov: “The Avenger”

·         Chinua Achebe: “Marriage is a Private Affair”

·         Nadine Gordimer: “Train from Rhodesia”

 

·         Wakako Yamuchai: “And the Soul Shall Dance”

Unit-3
Teaching Hours:12
Poetry
 

Poetry                                                                                                             12 hrs

·         Octavio Paz: “As One Listens to the Rain”

·         Jamaica Kincaid: “Girl”

·         Derek Walcott: “A Far Cry from Africa”    

 

·         Joseph Brodsky: “Freedom”

Unit-4
Teaching Hours:9
Essays
 

·         Alice Walker: Excerpts from “In Search of My Mother’s Gardens”

·         Hannah Arendt: “Men in Dark Times”

Dalai Lama Nobel Acceptance Speech

 

 

 

 

Text Books And Reference Books:

Blends Book II

Viktor Frankl's "Man's Search for Meaning"

Essential Reading / Recommended Reading

Elie Wiesel "Night"

Diary of Anne Frank

Famous Nobel Lectures

Evaluation Pattern

CIA 1:  A written test for 20 marks. It can be an Open Book test, a classroom assignment, an objective or descriptive test pertaining to the texts and ideas discussed in class.  

CIA2: Mid-semester written exam for 50 works

 

CIA 3: This is to be a creative test/ project in small groups by students. They may do Collages, tableaus, skits, talk shows, documentaries, Quizzes, presentations, debates, charts or any other creative test for 20 marks. This test should allow the students to explore their creativity and engage with the real world around them and marks can be allotted to students depending on how much they are able to link the ideas and discussions in the texts to the world around them.

CSC431 - COMPUTER NETWORKS AND JAVA PROGRAMMING (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course helps to understand the concepts of computer networks, network models and object oriented programming to develop real world applications. This course gives knowledge of various network components, protocols, architectures and its applications in data communication system.

Learning Outcome

CO1: Understand the basic concepts of computer networks and object-oriented programming.

CO2: Create small to medium-sized application programs that demonstrate professionally acceptable coding.

CO3: Analyze and design the practical solutions to the problem by applying Object-oriented concepts and network technologies

Unit-1
Teaching Hours:6
Introduction and Network Models
 

Data communications: components – Network criteria – physical structures – network models – categories of networks –interconnection of networks - Inter network Protocols and standards: protocols-standards-standards organizations- internet standards Network models: Layered tasks – OSI model – layers in the OSI model - TCP/IP protocol suite

Unit-2
Teaching Hours:7
Physical Layer
 

Data and Signals: Analog and Digital: Analog and Digital Data – Analog and Digital signals, periodic and non periodic signals - Transmission Impairment: Attenuation – Distortion – Digital Transmission: Digital to digital conversion: Line coding – line coding schemes – PCM – serial transmission – parallel transmission. Transmission Media Guided media: Twisted pair cable – coaxial cable – fiber optic cable - Unguided media: radio waves - micro waves – infrared

Unit-3
Teaching Hours:6
Data Link Layer
 

Error correction and detection: Introduction – checksum; Multiple Access: Random access – Aloha Controlled Access: reservation – polling – token passing. Channelization: FDMA - TDMA Wireless Lans IEEE 802.11 - architecture - MAC sub layer Addressing mechanism. Connecting Devices – hubs – repeaters – bridges – switches – routers – gateway.

Unit-4
Teaching Hours:6
Network Layer
 

Network Layer: Addressing IPV4 addresses - IPV6 Addresses - Internet Protocol: IPv4 – IPv6Address. mapping protocols: ARP – RARP Routing protocols: Unicast routing protocols: distance vector routing, Link State routing, Multicast Routing protocols

Unit-5
Teaching Hours:5
Transport Layer and Application Layer
 

Transport Layer: Process to process delivery – UDP – TCP. DNS: Name space – domain name space – distribution of name space Electronic mail Architecture – FILE transfer: FTP. WWW and HTTP: Architecture – web documents – HTTP.

Unit-6
Teaching Hours:6
Introduction to OOPs & Java
 

OOPs - Problems in Procedure Oriented Approach, Features  of  Object  Oriented  Programming System, OOPs Concepts-Class/Object, Encapsulation, Abstraction, Inheritance, Polymorphism, History and Evolution of Java, Overview of Java Data types, Variables, Arrays, Operators and Control structures.

Unit-7
Teaching Hours:6
Classes and Inheritance
 

Class fundamentals, objects, methods, constructors,  overloading  methods and constructors. Access control-static, Command line arguments, Inheritance & its  types,  super&  this keyword, Abstract class.

Self-Study: String class, Garbage Collection & finalize.

Unit-8
Teaching Hours:6
Packages and Interfaces
 

Defining packages, access protection, importing packages, defining, implementing interfaces. Nested interfaces. Inheritance and interfaces. Use of static in interfaces.

Unit-9
Teaching Hours:6
Exceptional Handling and introduction to multithreading
 

Fundamentals of exceptional handling, types of exceptions, uncaught exceptions, using try, catch, multiple catch clauses, nested try, throw, throws, finally, creating user defined exceptions, Chained exceptions, Introduction to multithreading - The Java Thread model, Thread priorities and Synchronization.

Unit-10
Teaching Hours:6
Input/Output, Enumerations, Type Wrappers
 

I/O - classes, interfaces, files and directories, Byte stream and character streams, Enumerations fundamentals, The values ( ) and valueOf( ) Methods, Type Wrappers -Character, Boolean, The Numeric Type Wrappers.

Text Books And Reference Books:

[1] Behrouz A Forouzan, Data communication and networking, 5 Edition, TMH Publications, 2017.

[2] Herbert Schildt, Java: The Complete Reference, Oracle Press, Tenth Edition, 2018

Essential Reading / Recommended Reading

[1] William Stallings, Data and Computer Communications, Tenth Edition, Pearson Publications, 2014.

[2] Andrew S Tanenbaum, Computer Networks, 5th Edition, PHI publication, 2012.

 [3] Larry L. Peterson and Bruce S Davie, Morgan Kaufmann, Computer Networks: A system approach, 2010.

[4] Core Java Volume-1 Fundamentals,Cay S. Horstmann,Pearson Education, Tenth edition ,2016.

Evaluation Pattern

CIA: 50%

ESE: 50%

CSC451 - JAVA PROGRAMMING LAB (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To enable the students to gain hands on experience in object-oriented programming using Java

Learning Outcome

CO1: Understand the basic concepts of Java Programming with emphasis on ethics and principles of professional coding.

CO2: Implement the concepts of code reusability and debugging

CO3: Develop applications using java

Unit-1
Teaching Hours:30
LIST OF PROGRAMS
 

1. Implement the concept of class

2. Implement the concept of arrays

3. Implement the concept of String Handling.

4. Implement the concept of method overloading.

5. Implement the concept of inheritance

6. Implement the Abstract class.

7. Implement the concept of interfaces

8. Implement the concept of packages

9. Implement the concept of exception handling.

10. Demonstrate usage of File Input Stream and File Output stream

Text Books And Reference Books:

[1] Herbert Schildt, Java: The Complete Reference, Ninth Edition,  2017 Oracle Press 

Essential Reading / Recommended Reading

[1] Cay S. Horstmann, Core Java Volume-1 Fundamentals, Tenth edition, 2016,  Pearson Education

Evaluation Pattern

CIA 50%

ESE 50%

ENG421 - ENGLISH-IV (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:2

Course Objectives/Course Description

 

 

This syllabus is meant to cater to all the three streams- B.A., B.Sc.and B.Com therefore the selection of units, has been done keeping in mind the general needs of students from these different backgrounds. Topics of universal concern, appeal and relevance have been included to sustain the interests of all students.

 

The selection of topics also progresses in complexity with each semester, enabling the students to gradually progress into more serious and sustained patterns of reading and become increasingly perceptive and conscious of their own selves and the world they see around them.In a nutshell we aim to bring out a text that will empower the holistic development of every student. 

 

 

 

In addition, the selection of topicsis also heavily based on skill sets identified to be taught. Topics are carefully chosen to integrate appropriate language and communication skills among students. The specific focus of these two semesters is to build employability skills among them and to this effect, we have career advancement skills and employability skills based units. The learners will be exposed to various skill sets required to be able to handle various requirements both in their academic and workplaces.

 

 

Course Objectives:   

 

·       To enable learners to develop reading comprehension for various purposes

 

·       To enable learners to develop writing skills for academic and professional needs

 

·       To enable learners to develop the ability to think critically and express logically

 

·       To enable learner to communicate in a socially and ethically acceptable manner

 

·       To enable learners, to read, write and speak with clarity, precision and accuracy

 

 

Learning Outcome

CO1: Ability to judge audience requirements in oral and written communication and communicate accordingly.

CO2: Ability to use specific styles in communication and understand workplace structures and requirements to communicate

CO3: Lead and participate in seminars and group discussions more effectively and with increased confidence.

Unit-1
Teaching Hours:10
Emotional Intelligence
 

 

Self-awareness

 

Stress management

 

Assertive skills

 

Critical thinking

 

Creative problem solving and decision making

 

 Appreciative inquiry

 

 Conflict resolution

 

Unit-2
Teaching Hours:10
Professional skills
 

 

Professional ethics and etiquette (cell phone etiquette)

 

Organisation skills

 

Research and information management

 

Teamwork

 

Leadership skills 

 

Workplace ethics- culture, values and gender (netiquette)job search skill, mindfulness, goal setting, self-awareness

 

Unit-3
Teaching Hours:10
Workplace skills
 

 

Interview skills

 

Professional etiquette

 

Elevator pitch

 

Teleconference

 

Video conference

 

Conference calls

 

Negotiation

 

Networking 

 

Unit-4
Teaching Hours:10
Feature writing
 

 

Writing for advertisement

 

Developing web content

 

Infographics

 

Emails 

 

Making notes in meetings

 

Minutes

 

Newspaper writing

 

Press release

 

Blog writing

 

Tender

 

Memo

 

Brochure

 

User manual

 

Text Books And Reference Books:

NIL

Essential Reading / Recommended Reading

ENGLOGUE 2

Evaluation Pattern

 

CIA 1: Classroom assignment/test/ written or oral tasks for 20 marks keeping in tune with the course objectives and learning outcomes.

 

CIA 2: Mid-semester for 50 marks.

 

CIA 3: Collage, tableaus, skits, talk shows, documentaries, Quizzes or any creative assignments.

End- semester 50 marks 

 

 

 

 

 

End Semester Exam: 2 hrs

 

 

 

 

 

FRN421 - FRENCH (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

French as a second language in the UG program. The method Génération A2 consists of a student's book and an activity book, both included in the digital manual. It consists of 6 units preceded by an initial section of 'Welcome'. Continuing from where A1 left, it aims to enhance learning skills further. The structure of each unit marks a real learning journey into different aspects of the French language and culture.

 

Course Objectives

·       To develop linguistic competencies and sharpen oral and written communicative skills further

·       To enhance awareness of different aspects of francophone civilization.

·       To enrich the learner’s vocabulary

·       To enable learners to engage in and discuss simple topics with ease

 

Learning Outcome

CO1: To familiarize students with the French culture and traditions.

CO 2: To equip students with correct grammar, vocabulary and pronunciation.

CO3: To enhance communicative skills.

CO 4: To make them well versed in all the four language skills.

CO5: To make them ready for A2 level Exams.

Unit-1
Teaching Hours:10
Festivals and traditions in France
 

Lesson 1: Let’s do the housework!

Lexicon – Lodging, the house, rooms

Grammar – The progressive present tense , possessive pronouns, negative form

Speech act – Protesting and reacting

 Lesson 2: About lodging

Lexicon – Furniture and equipment, household tasks

Grammar – Some adjectives and indefinite pronouns, verbs ‘to read, to break up

                   and to complain’

Speech act – Expressing interest and indifference

Unit-2
Teaching Hours:5
Drama
 

Molière’ s L’Avare – Français facile -Act III Sc 8 onwards

Unit-3
Teaching Hours:10
Culture and tradition
 

Lesson 1: All in form!

Lexicon – The human body: exterior / interior, sickness and medicines

Grammar – Simple past tense and imperfect, recent past, expression of duration

Speech act – Narrating in the past tense

Lesson 2: Accidents and catastrophes

Lexicon – Accidents, natural catastrophes

Grammar – Adjectives and indefinite pronouns: nothing, no one, verbs ‘to say,  to run, to die’

Speech act – Expressing fear and reassuring

 

Unit-4
Teaching Hours:5
Drama
 

Molière’ s L’Avare – Français facile -Act IV

Unit-5
Teaching Hours:10
French outside of France
 

Lesson 1: Studying abroad, Happy journey

Lexicon – The educational system, formalities to go abroad

Grammar – Demonstrative pronouns, simple future tense, situating in time

Speech act – Expressing one’s opinion,

 Lesson 2: The weather

Lexicon – The weather

Grammar –Me too, not me, impersonal verbs, verbs ‘ to believe, to follow and to rain’

Speech act – Speaking about the weather, speaking about the future

Unit-6
Teaching Hours:5
Drama
 

Molière’ s  L’Avare – Français facile -Act V

 

Text Books And Reference Books:

1.    Cocton, Marie-Noelle. Génération A2. Paris : Didier, 2016 

2.     Molière, L’Avare – Français facile

 

Essential Reading / Recommended Reading

1.     French websites like Bonjour de France, Fluent U French, Learn French Lab, Point du FLE etc.

 

Evaluation Pattern

Assessment Pattern

CIA (Weight)

ESE (Weight)

CIA 1 – Assignments / Letter writing / Film review

10%

 

CIA 2 –Mid Sem Exam

25%

 

CIA 3 – Quiz / Role Play / Theatre / Creative projects 

10%

 

Attendance

05%

 

End Sem Exam

 

50%

Total

50%

50%

HIN421 - HINDI (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course Description:

The detailed text-book "Ashad ka ek din” is a drama by Mohan Rakeshi, one of the eminent writers of modern Hindi Literature. Hindi journalismis is one of the major unit of this semester. Phrases, idioms, technical and scientific terminology are included in this semester to improve the literary skills.

Course Objectives:

Through the prescribed play and the theatre performance, students can go through the process of experiential learning. Study of Mass media enables them to get practical training. Phrases, idioms, technical and scientific terminology sharpen the language skills of the students.  

 

Learning Outcome

CO1 : Understand the nuances of Hindi theatre.

CO2: Create awareness of the social issues.

CO3: Improve the skill of critical analysis.

CO4: Develop the writing skills for media.

Unit-1
Teaching Hours:15
Natak- Ashad Ka Ek Din (Play) by Mohan Rakesh
 

Madhavi (Play) ByBhishma Sahni. Rajpal and Sons, New Delhi - 110006 

Level of knowledge: Analitical

Unit-2
Teaching Hours:15
SancharMadhyam
 

  •  Report writing,
  • Media Interview                                                                    
  •  Hindi Journalism 
  • Electronic media and Hindi,
  • Print media                                    

Level of knowledge: Conceptual

Unit-3
Teaching Hours:15
Phrases, Idioms. and Scientific and Technical Terminology
 

1. 50 Nos. Phrases and Idioms for writing the meaning and sentence formation.  

2. 100 Nos. (Hindi equivalent)

Level of knowledge: Basic

Text Books And Reference Books:

  1. "Ashad ka ek din ” is a drama by Bhisma Sahni. Rajpal and Sons, New Delhi - 110006
Essential Reading / Recommended Reading

 1. News reporting and writing:          By Mencher,Melvin..

2. Hindi PatrakaritakaIthihas:By Jagadeesh Prasad Chaturvedi

3. HindiPatrakaritaSwaroopEvamSandarbh:                          By Vinod Godare

4. Media Interview:                     By Philip Bell,Theovanleeuwen.

 

Evaluation Pattern

CIA-1(Digital learning)

CIA-2(Mid sem exam)

CIA-3((Wikipedia-Article creation)

End sem exam

KAN421 - KANNADA (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:50
Credits:03

Course Objectives/Course Description

 

The course introduces the rich Kannada language and helps students to read and write the Regional language effectively. The prescribed text ‘Kalagnani Kanaka’ (Kanaka, the visionary) is all about 15th century poet, saint and philosopher of the Haridasa Bhakti tradition. “Kanaka’s writings touch on all aspects of truth and social reality’ said K.R. Nagaraj, literary critic and the author of the Kalagnani Kanaka play. “Kanaka’s poetry is dense with rhyme, rhythm, meter and rich descriptions. He upholds social justice while addressing the issues of the time-caste and class differentiation and gender oppression, for example. Contrary to popular belief, he never confined himself to any one philosophical tradition- Advaita, Dwaita or Vishistadwaitha” ‘Kannadada Moovattu Kathegalu’ is another prescribed text. Through this text the students are exposed to the writings of Koradkal Sreenivasa Rao, K. P. Poornachandra Tejaswi, Masti Venkatesha Iyengar, G. P. Basavaraj and others. Short stories help students in harnessing creative writing skills.

Learning Outcome

CO1: Reflects the tradition of old & the new

CO2: Helps to create dialogue writing

CO3: Identify key points in stories

CO4: Understand the ideologies during British rule

CO5: Expose to Dasa Sahitya movement

Unit-1
Teaching Hours:20
Kalagnani Kanaka- K.R. Nagaraj
 

Act- 1

Act- 2 

Act- 3 

Act- 4 

Act- 5

Act- 6

Unit-2
Teaching Hours:20
Selected short stories (Kannadada Moovatttu Kathegalu) Edited by: Fakir Mohammed katpadi, Krishnamurthy Hanur Publication: Sahitya Academy,2018
 

1.      Dhaniyara Sathyanarayana-Koradkal Sreenivasa Rao

2.      Thabarana Kate- K. P. Poornachandra Tejaswi

3.      Gowthami Helida Kathe- Masti Venkatesha Iyengar

4.      Raja mattu Hakki- G. P. Basavaraj

Unit-3
Teaching Hours:5
Language Skills
 

Essay Writing/ Letter Writing/ Dialogue writing 

Text Books And Reference Books:

1.      Adhunika Kannada Nataka: K.M. Marualasiddappa

2.      Kannada Rangabhoomi; L.S. Shesshagiri Rao

3.      Kannada Sanna Kathegala Olavu- Giradi Govinda Raju

4.      Tabarana Kathe- Kannada Screen play by Girish Kasaravalli

 

Essential Reading / Recommended Reading

1.      Adhunika Kannada Nataka: K.M. Marualasiddappa

2.      Kannada Rangabhoomi; L.S. Shesshagiri Rao

3.      Kannada Sanna Kathegala Olavu- Giradi Govinda Raju

4.      Tabarana Kathe- Kannada Screen play by Girish Kasaravalli

 

Evaluation Pattern

CIA- Wikipedia Article writing -20 marks

CiA-2 Mid Semester Exams- 50 marks

CIA-3 Wikipedia Article writing- 20 marks

End Semester Exams- 50 marks

MAT431 - ALGEBRA (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course description : This course aims at developing the ability to write the mathematical proofs.  It helps the students to understand and appreciate the beauty of the abstract nature of mathematics and also to develop a solid foundation of theoretical mathematics.

Course objectives : This course will help the learner to

COBJ1. Understand the fundamentals of groups and its theories.

COBJ2. Relate abstract algebraic constructs to more familiar sets and operators

COBJ3. Know about the subgroups and group homomorphisms

COBJ4. Get familiar with the theories on rings, integral domains and fields.

Learning Outcome

CO1: Describe and generate groups, rings and fields.

CO2: Identify and differentiate different structures and understand how changing properties give rise to new structures.

CO3: Demonstrate the knowledge of concepts of rings and fields.

Unit-1
Teaching Hours:15
Groups
 

Definition and examples of groups, examples of abelian and non-abelian groups, the group Zn of integers under addition modulo n and the group U(n) of units under multiplication modulo n, complex roots of unity, groups of symmetries: Equilateral triangle.

Unit-2
Teaching Hours:25
Subgroups and Group Homomorphisms
 

Subgroups, the concept of a subgroup generated by a subset and the commutator subgroup of group, examples of subgroups including the center of a group. order of an element, cyclic subgroups, Cosets, Index of subgroup, Lagrange’s theorem, consequences of Lagrange’s theorem, Normal subgroups: their definition, examples, and characterizations, Quotient groups, permutation groups and Symmetric groups – Homomorphism of groups – Kernel of group homomorphisms and theorems thereon – Fundamental theorem of homomorphism of group.

Unit-3
Teaching Hours:20
Rings, Integral Domain and Fields
 

Definition and examples of rings, examples of commutative and non-commutative rings: rings from number systems, Zn the ring of integers modulo n, ring of real quaternions, rings of matrices, polynomial rings, and rings of continuous functions. Subrings and ideals, Integral domains and fields, examples of fields: Zp, Q, R, and C. Field of rational functions.

Text Books And Reference Books:
  1. I. N. Herstein, Topics in Algebra, Second Edition. Wiley India (P) Ltd. New Delhi, India Vikas Publishing House Pvt. Ltd, 2006.
Essential Reading / Recommended Reading
  1. M. Artin, Abstract Algebra, 2nd Ed., Pearson, 2011.
  2. S. R. Nagpaul and S.K.Jain, Topics in Applied Abstract Algebra, Universities Press, 2010.
  3. Joseph A Gallian, Contemporary Abstract Algebra, 4th Ed., Narosa, 2000.
  4. Pinter, Charles C. A Book of Abstract Algebra, New York: McGraw-Hill, 1990.
  5. J. B. Fraleigh, A First Course in Abstract Algebra, 7th Ed., Pearson, 2002.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ,

Written Assignment,

Reference work, etc.,

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT451 - PYTHON PROGRAMMING FOR MATHEMATICAL MODELLING (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: The course Python programming for mathematical modelling is aimed at enabling the students study the implementation of Python programming for solving some real world problems. It is designed with a learner-centric approach wherein the students will acquire mastery in the modelling and simulation by using Python programming language as a tool.

Course objectives: This course will help the learner to

COBJ1. Acquire proficiency in using Python to present data grapically

COBJ2. Solving differential equations analytically and numerically using Python.

COBJ3. Acquire skills to solve various Mathematical models- exponential growth, Logistic growth, simple pendulum and spreading of disease.

Learning Outcome

CO1: Solve differential equations governed by mathematical models using Python.

CO2: Demonstrate the use of Python to interpret and analyze the data.

Unit-1
Teaching Hours:30
Proposed Topics
 
  1. Plots -2D and 3D, graph customization.
  2. Solving calculus problems: functions, limits, continuity, and derivatives.
  3. Application of derivatives: cost function, revenue function, marginal cost, marginal revenue.
  4. Differential equations in sympy.
  5. Solution of initial value problems.
  6. Mathematical models using linear differential equations interest rate- Population growth.
  7. Python program for data management (Library, Bank, Billings).
  8. Case Study.
Text Books And Reference Books:
  1. H P Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer , 2016.
  2. A. Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, No Starch Press, 2015.
Essential Reading / Recommended Reading
  1. B E Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. C Hill, Learning Scientific Programming with Python, Cambridge University Press, 2016.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

SAN421 - SANSKRIT (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Avimarakam by Bhasa is the drama  prescribed as a text and approved in the B.O.S.  It is sociological drama which explains about the society.  . This drama is an imaginary composition of Bhasa . The concept and drama skills expresses the beauty of the style of the author Bhasa.  He creates the characters and the incidents are naturally created. Grammar will also be studied.

Learning Outcome

CO1: To Understand the style and development of the play

CO2: To learn the linguistic skills of the drama.

CO3: To Deliberate the classification and characteristics of the play

CO4: To Understand the features of play

CO5: To understand the basic structural nuances of Panini?s grammar

Unit-1
Teaching Hours:35
Canto 1-5
 

Avimarakam of Balagovindaha  Jha Origin and development of Nataka to understand the different theories and original nature of Sanskrit dramas. Avimarakam  by Balagovind jha  provides an insight to sociological life .Basic grammer only rules are given for usage in composition. Language component will help for proper usage of Sanskrit language.

             Level of knowledge: Basic/conceptual/ Analytical

Avimaraka meeting kurangi and Avimaraka engtering into the mansion of  Kurangi

Unit-2
Teaching Hours:5
Grammar
 

Karaka prakaranam 

Vykarana vishesha 

Unit-3
Teaching Hours:5
Language skills
 

Translation of given passage from English to Sanskrit

Writing an article in Sanskrit on the given topics

Text Books And Reference Books:

Avimarakam  by Balagovind jha 

Essential Reading / Recommended Reading

            

Books for Reference: -

1.      “Avimarakam” by Balagovinda Jha

2.      Basanatakachakram  of choukamba edition.

3.      Sanskrit dramas by a.B.Keith

4.      Sanskrit grammar by M.R.Kale.

Evaluation Pattern

CIA 1 Wikipedia assignments

CIA 2 Mid semester examinations

CIA 3 Wikipedia assignments

STA431 - ELEMENTS OF STOCHASTIC PROCESS (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 

This course designed to introduce the concepts, models and problem solving techniques of stochastic process.

Learning Outcome

CO1: Solve the problems related to business or industry which are stochastic in nature.

CO2: Demonstrate the different queuing systems and methods to solve the queuing problems.

Unit-1
Teaching Hours:10
Introduction Stochastic Processes
 

 

Conational Probability – Conditional Expectation: Discrete Case – Continuous case – Total Probability law – Bayes’ Theorem – Computing Expectations by conditioning - Stochastic processes – Classification of Stochastic Processes.

Unit-2
Teaching Hours:15
Markov Chains
 

Definition of Markov Chain - transition probability matrix - order of Markov chain – Chapman- Kolmogorov equations - classification of states and chains – Long-Run proportions and limiting probabilities – Mean time spent transient states – Branching processes – Time Reversible Markov chains – Markov Chain Monte Carlo Methods.

Unit-3
Teaching Hours:15
Poisson Process
 

 

Exponential Distribution – Counting process – Poisson Process – Interarrival and waiting time distributions – Properties of Poisson processes – Conditional Distribution of the arrival times – Continuous Time Markov Chains – Birth and Death Processes – Limiting Probabilities.

Unit-4
Teaching Hours:10
Queuing System
 

Cost equations – Steady-State Probabilities – Exponential models: Single-Server exponential queuing system - Single-Server exponential queuing system having finite capacity – Birth and death queuing model.

Unit-5
Teaching Hours:10
Simulation
 

General Techniques for simulating continuous random variables – The inverse transformation method – The Rejection method – Normal distribution – Gamma distribution – Chi-Square distribution – Beta (n, m) distribution – Exponential distribution.

Text Books And Reference Books:

Sheldon M. Ross, Introduction Probability Models, 11th Edition, Academic Press, 2016.

Essential Reading / Recommended Reading

 

  1. Medhi J, Stochastic Process, New Age International Publishers, 2009.

  2. Basu A.K, Introduction to Stochastic Process, Narosa Publications, 2005.

  3. Bhat B.R, Stochastic Models: Analysis and Applications, New Age International Publishers, 2004.

Evaluation Pattern

Evaluation Pattern

CIA 50%

ESE 50%

STA451 - ELEMENTS OF STOCHASTIC PROCESS PRACTICAL (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

This course is designed to teach practical Stochastic process problems using statistical softwares.

Learning Outcome

CO1: Demonstrate and evaluate stochastic models using statistical softwares.

Unit-1
Teaching Hours:30
Practical assignments
 

 

  1. Calculation of conditional and joint probabilities

  2. Illustration of Bayes’ Theorem

  3. Construction of Transition probability matrix

  4. Stationarity of Markov chain and graphical representation of Markov chain

  5. Computation of probabilities in case of generalizations of independent Bernoulli trials

  6. Illustration of Poisson Process

  7. Calculation of probabilities for given birth and death rates and vice versa

  8. Calculation of probabilities for Birth and Death Process

  9. Single-Server exponential queuing system

  10. Single-Server exponential queuing system having finite capacity.

  11. Simulation using Normal and Gamma distributions

  12. Simulation using Beta and Exponential distributions

Text Books And Reference Books:

 

Sheldon M. Ross, Introduction Probability Models, 11th Edition, Academic Press, 2016.

Essential Reading / Recommended Reading

 

  1. Medhi J, Stochastic Process, New Age International Publishers, 2009.

  2. Basu A.K, Introduction to Stochastic Process, Narosa Publications, 2005.

  3. Bhat B.R, Stochastic Models: Analysis and Applications, New Age International Publishers, 2004.

Evaluation Pattern

CIA 50%

ESE 50%

TAM421 - TAMIL (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

A new concept, cultural studies, will take the students beyond prescribed syllabus to include music, theatre, painting, and films out of which the art form of music is taken up for the first semester.  Aram poetry- Ara nericharam specifies life discipline and standards, which would pave a successful life for the students. 

Bhakthi ilakiya- them bhavani, cheerapuranam, thirumandiram is inclined towards ritual practices. Kaapiyam with its historical values provides an understanding about life in a mature way.



Learning Outcome

CO1: Recall and categorize the concepts of literature.

CO2: Understand the true essence of the texts, and inculcate them in their daily lives.

CO3: Recognize and apply the moral values and ethics in their learning.

CO4: Comprehend the concepts in literature and appreciate the literary text.

Unit-1
Teaching Hours:10
Kappiyam
 

seevaga sindhamani.

Thirumular Thirumandhiram

These topics coherently plays a significant role in inclination towards spiritual aspects of life. It puts for the religious beliefs and entitles each one to understand the rituals and practices.

Unit-2
Teaching Hours:10
Ara illakiyam
 

Aranericharam- Munai padaiyaar

The text acustoms the core values and ethics with the ideological guidelines and ways of living.

Unit-3
Teaching Hours:10
Bakthi illakiyam
 

Thembavani

Seera puranam

Thiru mular, thiru mandhiram

The text elicits the importance of rituals and beliefs. 

 

Unit-4
Teaching Hours:10
Prose
 

Nadagam

1. Irakam yenge- C N Anna Dhorai

2. Theervu - Indhra partha sarathi

3. Soothradharam- Puvi Arasu

4. Karumbum Kalliyum- Komal saminadhan

5. Palaavku thookigal - Dr. A. Ramasamy

6. Pei ottam- Dr. K A Guna Sekaran

 

Unit-5
Teaching Hours:1
Grammer
 

Vetrumai orupugal

Unit-6
Teaching Hours:4
Common topic
 

Tamizhil pudhirgalum, pazhamozhigalum

Text Books And Reference Books:

1. Neethi book, Manikkavasakar pathippakam, paarimunai, Chennai -08 

2. Tamil paa thirattu - prasaranga pub. Bangalore university, Bangalore 

3. Kappiya noolkal-manikkavasakar pathippakam, Chennai -08 

4. Madagascar kalanchiyam - van a thing pathippakam

 

Essential Reading / Recommended Reading

1. Thamil paa thirattu - prasaranga pub. Bangalore university, Bangalore 

2. Mozhi varalaru - Dr. My. Varatharajan - kazhaka pub. Chennai- 01 

3. Aranerichaaram-Munaipatiyaar 

4. Kazhaka pub. Thirunelveli, thenninthiya saivachiththantha noorpathippu kazhaka, Ltd., Chennai 01 

5. Thirumoor thirumandiram-Thiruvaavatuthurai aathinam, Thiruvaavatuthurai Nadagam, Education in karnataka Bangalore 01. 

6. Madras university , etaikkala illakkiyam, Chennai -01 

7. Thamizh pazhamozhikal, janaral pub. Mylappur, Chennai -04 

8. Thamizhil puthirkal our aayivu-Aaru. Ramanadan, Manikkavasakar niilakam, Chennai -01

 

Evaluation Pattern

 

 

EXAMINATION AND ASSIGNMENTS: There is a continuous evaluation both at the formal and informal levels. The language skills and the ability to evaluate a text will be assessed

This paper will have a total of 50 marks shared equally by End Semester Exam (ESE) and Continuous Internal Assessment (CIA) While the ESE is based on theory the CIA will assess the students' critical thinking, leadership qualities, language skills and creativity

 

CSC541A - DATA ANALYTICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is to help students learn, understand, and practice data analytics from the chosen domain. The better understanding of Data, Relations, Preprocessing, Visualization, Correlation, Regression and Clustering plays an important role to find a formidable answer for any kind of applications.

Learning Outcome

Upon successful completion of the course the student will be able to:

CO1: Demonstrate the fundamental principles of data analytics

CO2: Apply appropriate preprocessing and visualization techniques on the data set

CO3: Design data analytic models for effective decision-making

Unit-1
Teaching Hours:9
Data and Relations
 

The Iris Data Set - Data Scales - Set and Matrix representations - Relations - Dissmilarity measures - Similarity measures - Sequence Relations - Sampling and Quantization.

Unit-1
Teaching Hours:9
Introduction
 

Data Analysis, Data Mining and Knowledge Discovery. 

Unit-2
Teaching Hours:9
Data Visualization
 

Diagrams - Principal Component Analysis - Multidimensional Scaling - Associator - Histograms.

Unit-2
Teaching Hours:9
Data Preprocessing
 

Error types - Error Handling - Filtering - Data Transformation - Data Integration.

Unit-3
Teaching Hours:9
Regression
 

Linear Regression - Linear Regression with Nonlinear Substitution - Robust Regression - Neural Networks - Radial Basis Function Networks - Cross-Validation - Feature Selection.

Unit-3
Teaching Hours:9
Correlation
 

Linear Correlation - Correlation and Causality - Chi-square Test for Independence.

Unit-4
Teaching Hours:9
Forecasting
 

Finite State Machines - Recurrent Models - Autoregressive Models.

Unit-4
Teaching Hours:9
Classification
 

Classification criteria - Naive Bayes Classifier - Linear Discriminant Analysis - Support Vector Machine - Nearest Neighbor Classifier - Decision Trees.

Unit-5
Teaching Hours:9
Optimization Methods
 

Optimization with Derivatives - Gradient Descent.

Unit-5
Teaching Hours:9
Clustering
 

Cluster Partitions - K-Means Clustering - Hierarchy Clustering - Prototype-Based Clustering - Fuzzy Clustering - Relational Clustering - Cluster Tendency Assessment - Cluster Validity - Self-Organizing Map.

Text Books And Reference Books:
  1. Runkler, Thomas. A, "Data Analytics: Models and Algorithms for Intelligent Data Analysis", Springer, 2012.
  2. Anil Maheswari, "Data Analytics", McGraw-Hill Education, First Edition, 2017.
Essential Reading / Recommended Reading
  1. Soraya Sedkaoui, "Data Analytics and Big Data", John Wileys & Sons, 2018.
  2. Robert Keane, "Data Analytics: Master the Techniques for Data Science, Big Data and Data Analytics", CreateSpace Independent Publishing Platform, 2017.
  3. Herbert Jones, "Data Analytics": An Essential Beginners Guide to Data Mining, Data Collection, Big Data Analytics for Business, and Business Intelligence Concepts", CreateSpace Independent Publishing Platform, 2018.
Evaluation Pattern

CIA - 50%

ESE - 50%

CSC541B - INTERNET OF THINGS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To explore students to the world of interconnected devices, communication among these connected devices, transfer of data and further analysis of this data to make appropriate decisions

Learning Outcome

CO1: Analyze the functional blocks involved in Internet of Things.

CO2: Understand the architecture of Internet of Things.

CO3: Infer the process of analysing data in Internet of Things.

CO4: Demonstrate the application of IoT in real world.

Unit-1
Teaching Hours:9
Introduction to Internet of Things
 

Introduction, Definition and Characteristics of IoT, Physical Design  of  IoT,  Things in IoT, IoT Protocols, Logical Design of IoT, IoT Functional Blocks,  IoT Communication Models,  IoT Communications APIs, IoT Enabling Technologies, Wireless Sensor Networks, Cloud Computing, Big Data Analytics, Communication Protocols, Embedded Systems.

Unit-2
Teaching Hours:9
IoT Physical Devices and EndPoints
 

What is an IoT Device, Exemplary Device: Raspberry Pi, About the Board, Linux on  Raspberry Pi, Raspberry Pi interfaces, Programming Raspberry Pi with Python. Other IoT Devices – pcDuino, BeagleBone Black, Cubieboard.

Unit-3
Teaching Hours:9
Domain Specific IoTs and M2M
 

Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry, Health & Lifestyle.

IoT and M2M – Introduction, M2M, Difference between IoT and M2M, SDN and NFV for  IoT.

Unit-4
Teaching Hours:9
Arduino Programming
 

The Arduino Ecosystem, Installing the software, Connecting the Arduino, Opening a sketch, Sketching in code, The Structure of Arduino C, Verifying and Uploading, Working with variables, Making Decisions, Digital Ins and Outs, Analog In, Analog Out.

Unit-5
Teaching Hours:9
Infrastructure and Service Discovery Protocols for the IoT Ecosystem
 

Infrastructure Protocols: Routing Protocol, IEEE 802.15.4, Bluetooth Low Energy, Z-Wave, ZigBee. Protocols for IoT Service Discovery: multicast Domain Name System (mDNS), DNS Service Discovery, Universal Plug and Play. Prominent IoT Service Discovery Products available in the market.

Text Books And Reference Books:

[1]   Arshdeep Bahga and Vijay Madisetti , "Internet of Things: A Hands-on Approach", Universities Press, 2015

   [2]   Pethuru Raj and Anupama C. Raman , “The Internet of Things: Enabling Technologies, Platforms, and Use Cases", CRC Press, 2017.

   [3]  Brian Evans, Beginning Arduino Programming, Apress, 2011

Essential Reading / Recommended Reading

[1]  March Schwartz, “Internet of Things with Arduino Cookbook”, Packt Publishing, 2016

   [2]   Olivier Hersent , David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications and Protocols”, Wiley, 2012.

[3]Peter Waher, “Mastering Internet of Things: Design and create your own IoT applications using Raspberry Pi 3”, Packt Publishing, 2018

Evaluation Pattern

CIA 50%

ESE 50%

CSC541C - DIGITAL IMAGE PROCESSING (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course helps student to understand image enhancement techniques in spatial domain.This also focuses on classification of images using Matlab

Learning Outcome

CO1: Understand the enhancement techniques of images.

CO2: Analyse different filtering methods in Spatial domain.

Unit-1
Teaching Hours:30
List of programs
 

1.     Write a program to display frequency of each pixel occurring in a row of an image.

2.     Write a program to convert color images to Gray scaleImages.

3.     Write a program to perform Rotation of images using differentmethods.

4.     Write a program to perform resizing of images using differentmethods.

5.     Write a program to implement Contraststretching

6.     Write a program to demonstrate smoothening of animage

7.     Write a program to perform non-linear filtering of animage(Median)

8.     Write a program to implement of Edgedetection

9.     Write a program to extract the three color components in theimages

10.  Write a program to perform bit planeslicing.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA 50%

ESE 50%

CSC541D - BUSINESS INTELLIGENCE (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Effective implementation of a Business Intelligence (BI) results in better business decisions and increased success in achieving goals. Business intelligence is the process of collecting and turning the resource into business value. This course will provide an understanding of business intelligence, knowledge delivery and examine the BI processes and techniques used in transforming data to knowledge and value.

Learning Outcome

CO1: Understand the fundamentals of business intelligence.

CO2: Apply various modeling techniques and business intelligence methods to various situations using data mining principles.

CO3: Demonstrate the impact of business reporting, information visualization, and dashboards.

Unit-1
Teaching Hours:9
Introduction to Business Intelligence
 

Introduction to OLTP and OLAP, BI Definitions & Concepts, Business Applications of BI, BI Framework, Role of Data Warehousing in BI, BI Infrastructure Components – BI Process, BI Technology, BI Roles & Responsibilities.

Unit-2
Teaching Hours:10
Basics of Data Integration ETL
 

Concepts of data integration need and advantages of using data integration, introduction to common data integration approaches, introduction to ETL, Introduction to data quality, data profiling concepts and applications.

Unit-3
Teaching Hours:9
Introduction to Multi-Dimensional Data Modeling
 

Introduction to data and dimension modeling, multidimensional data model, ER Modeling vs. multi-dimensional modeling, concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake schema.

Unit-4
Teaching Hours:9
Basics of Enterprise Reporting
 

Introduction to enterprise reporting, concepts of dashboards, balanced scorecards, and overall architecture.

Unit-5
Teaching Hours:8
Data Mining Functionalities
 

Association rules mining, Mining Association rules from single level, multilevel transaction databases, Classification and prediction, Decision tree induction, Bayesian Classification, knearest neighbour classification.

Text Books And Reference Books:

 [1] CindiHowson ,Successful Business Intelligence, Unlock the Value of BI & Big Data Hardcover –Second Edition: Import, 1 Nov 2013. 

Essential Reading / Recommended Reading

[1] Gert H.N. Laursen, JesperThorlund , Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback , 26 Sep 2013 

Evaluation Pattern
CIA: 50%
ESE: 50%

   

CSC542A - UNIX OPERATING SYSTEM (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

UNIX is a multi user and multi-tasking operating system. After learning the concepts of an operating system, it is appropriate to learn how UNIX implements these concepts. The subject is introduced with the features and architecture of UNIX. The file system, Process management and Memory management are discussed to make the students understand the internals of UNIX. Various commands used by UNIX shell is also discussed which makes the users of UNIX comfortable to interact with each other. Bourne shell programming is dealt in depth which can be used to develop applications in UNIX.

Learning Outcome

CO1: Describe the architecture and features of UNIX Operating System and distinguish it from other Operating System.

CO2: Apply and change the ownership and file permissions using advance Unix commands.

CO3: Build Regular expression to perform pattern matching using utilities like grep, sed and awk.

CO4: Implement shell scripts for real time applications.

Unit-1
Teaching Hours:8
Introduction to UNIX
 

Evolution of UNIX – UNIX System Structure – Features of Unix - Operating System Services - Unix Kernel - Locating Commands, Internal and External Commands, Flexibility of Command Usage, man: Browsing and Manual Pages On-line, Understanding the man Documentation.

General Purpose Utilities: cal, date, echo, printf, echo, bc, script, passwd, who, uname, tty, stty.

Unit-2
Teaching Hours:9
The UNIX file system
 

The File, I-nodes – Structure of a regular file. The HOME Variable: The Home Directory, Directory related commands: pwd, mkdir, cd, rmdir. Absolute and relative path names. The UNIX File System. File manipulation commands: cat, cp, rm, mv, more, The lp Subsystem: Printing a File, file, wc, Words and Characters, od, The spell and ispell, cmp, comm, diff. File compression commands: gzip, gunzip, tar, zip, unzip. Basic file attributes: The –d Option: Listing Directory Attributes, File Ownership, File Permissions, chmod: Changing File Permissions, Directory Permissions, Changing File Ownership. Hard links, Symbolic Links, ln, umask, and find.

Unit-3
Teaching Hours:9
UNIX process management
 

Process Basics, Process States and Transitions, ps: Process Status, System Processes (-e or –a), Internal and External Commands, Running Jobs in Background, nice: Job Execution With Low Priority, Killing Processes with Signals, Job Control, at and batch: Execute Later, cron: Running Jobs Periodically, time: Timing Processes. PID and PPID.

Unit-4
Teaching Hours:9
Filters and communication simple filters
 

The Sample Database, pr: Paginating Files, head: Displaying the Beginning of a File, tail: Displaying the End of a File, cut: Slitting a File Vertically, paste: Pasting Files, sort: Ordering a File, uniq: Locate Repeated and Non repeated Lines, tr: Translating Characters, An Example: Displaying a Word-count List. Filters using regular expressions: grep: Searching for a Pattern, and egrep. Communication: Communicating with Other Users: Who, Mail, Wall, Send, Mesg, Ftp.

Unit-5
Teaching Hours:10
UNIX shell environment
 

The Wild-cards, Escaping and Quoting, Redirection; Review of vi Operations – Different Modes – Saving and Exiting - Accessing Multiple Files - Interacting with Unix - Miscellaneous Commands - Alphabetical List of Keys. Shell variables - Shell Keywords - Positional parameters - Passing command line arguments. Arithmetic in shell scripts - Read and Echo - Control Structures - if-then-fi - if-then-else-fi - Nested if - Case control structure – Loops - while-until –for - break and continue. Shell meta characters - Exporting variables - User defined Functions.

Text Books And Reference Books:

[1] Sumitabha Das. UNIX Concepts and Applications. 5th Edition, New Delhi: Tata McGraw Hill, 2013.

[2] Yashavant P Kanetkar. Unix Shell Programming. New Delhi: BPB Publications, 2012.

Essential Reading / Recommended Reading

[1] Maurice J Bach. The Design of Unix Operating System. NewDelhi: Prentice Hall of India Pvt. Ltd, 2012.

[2] Paul Love, Joe Merlino, Craig Zimmerman, Jeremy C. Reed, and Paul Weinstein. Beginning UNIX. New Delhi: Wiley Publishing, Inc, (Wrox Publishing) 2007

Evaluation Pattern

CIA - 50%

ESE - 50%

CSC542B - WEB TECHNOLOGY (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is designed to introduce the students to web technologies in Hyper Text Mark-up Language, Cascade Style Sheet, JavaScript and PHP for interactive web applications that use rich user interfaces and also understand the server-side web technologies for creating dynamic web applications. Student will learn the concepts of web site planning and hosting. This course will help them to create an interactive website with great look and functionality.

Learning Outcome

CO1: Understand the World Wide Web and associated technologies.

CO2: Apply web development techniques for designing web pages.

CO3: Design an interactive website with web tools and scripting methods.

Unit-1
Teaching Hours:7
Web programming Introduction
 

 

Web technology terminology-Structure of web page- webpage-website-web server-work flow model. HTML5- History-Tags-Attributes-element-Basic tags –Formatting tags- Color coding

 

HTML FORMS

List – Images- Hyperlink-Table-Header-Introduction to advanced tags-input tags-forms-style-buttons-image-video

Unit-2
Teaching Hours:12
CSS Introduction
 

Cascading style sheet –Benefits –CSS version history-Syntax-External-internal-inline-single style-multiple style-value lengths and percentage-ID selector –Class Selector-group Selector – universal selector- Color-background-cursor-list-Box model-display positioning-floats

 

JavaScript – Fundamentals

Introduction to JavaScript-Client side-Server side-Advantages-limitations-Syntax-whitespace-line breaks-case sensitivity-comments-enabling in web browsers-placement-variables-executing first program-Data types –variables-scope-operators-if –else-switch-loops-function-events.

Unit-3
Teaching Hours:9
JavaScript Advanced
 

Event Handling-onclick-onsubmit-onmouseover-onmouseout-HTML 5 standard events-cookies-how it works-storing cookies-page redirect-page printing-JS objects-Boolean-string

 

 

 

Unit-4
Teaching Hours:10
PHP SCRIPTING
 

PHP syntax and variables, Operators and Expressions, Conditional Branching and Looping Statements - Essentials of PHP- Installation of Web Server, XAMPP Configurations-PHP Forms- GET and POST method

 

Unit-5
Teaching Hours:7
Instant Design
 

Create website using Instant Design tool-Create website using WIX/Webflow/Google Site-Creating responsive web pages

Testing and hosting

 

Sandboxing-Testing the website-cross platform browser compatibility check up. Templates usage (case study)

 

Text Books And Reference Books:

 

[1] Rachel Andrew, Jeremy Keith, “HTML5 for Web Designers”, Second Edition, 2nd Edition, 2016, ISBN: 9781492017899, Publisher - A Book Apart

[2] “CSS3 in easy steps”, Mike McGrath, publisher:  In Easy Steps,  ISBN: 9781840785418, 1840785411

[3] Jeremy McPeak and Paul Wilton,  “Beginning JavaScript”, Wrox publication,

Essential Reading / Recommended Reading

 

[1]. Faithe Wempen, Microsoft,” Start Here! Learn HTML5" , 2012

[2] David McFarland, O’REILLY , “CSS 3 Missing Manual”, 2nd edition , 2014

Evaluation Pattern

Weightage

-CIA-50%

-ESE-50%

CSC542C - MOBILE APPLICATIONS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course focuses on developing applications for modern Smartphone operating systems. Most of the course is dedicated to Google's Android. Rapid application development techniques are covered, as well as setup of the development environment, real-world testing, and deployment.

Learning Outcome

Upon completion of this course, Students will be able to:

CO1: Install and configure Android application development tools.

CO2:  Design and develop user Interfaces for the Android Application

CO3:  Develop and Deploy Android Applications using Multimedia Concepts

 

Unit-1
Teaching Hours:10
Overview
 

A little background about mobile technologies, Different mobile technologies – Android, Windows, IOS, Black Berry, series 40, Bada, Benefits and drawbacks of Smartphone programming, Overview of Android, How it all got started, Why Android different and important, Android Stack overview, Linux kernel, native libraries, App framework, Apps, SDK overview, platforms, tools, versions. Creating and setting up custom Android emulator.

Unit-2
Teaching Hours:8
Introduction to Android
 

Install the android SDK, Install base tools, install SDKs and Add-ons, Install apache Ant, Emulator, and Device. Get know Eclipse, Build , install and Run the Application in your Emulator or Device, Project Structure.

Unit-3
Teaching Hours:12
Designing User interface
 

Designing by declaration, creating the opening screen, using alternate resources, implementing an about box, applying a theme, adding a menu, adding settings, debugging with log messages, debugging with debugger.

Unit-4
Teaching Hours:8
Exploring 2D graphics and Multimedia
 

Learning the basics, adding Graphics to existing apps, handling input, learn to change the final improvements, Playing audio, Playing Video, Adding sound to existing app

Unit-5
Teaching Hours:7
Storing local Data
 

Reading/writing local data, Accessing the Internal File system, Accessing SD card.

Text Books And Reference Books:

 [1] Grant Allen, Beginning Android 4, Apress, 2012.

 [2] Wei-Meng Lee, Beginning android 4 application Development, John Wiley & sons, Inc, 2012.

Essential Reading / Recommended Reading

 [1] Ed Burnette, Hello, Android: Introducing Google's Mobile Development Platform, Pragmatic. Bookshelf (2009), ISBN-13: 978-1934356173.

 [2] Jerome (J.F) DiMarzio , Android - A programmer's Guide, TataMcgraw Hill, ISBN: 9780071070591, 2010.

 

Evaluation Pattern

CIA - 50%

ESE - 50%

CSC542D - GRAPHICS AND ANIMATION (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To acquire knowledge in three-dimensional modelling and animation using 3DS Max software and to render the animated scene effectively using light and material design.

Learning Outcome

Upon completion of the course students will be able to:

CO1: Understand the concept of 2D and 3D.

CO2: Construct graphic models in 2D and 3D with lighting effects.

CO3: Apply animation on 3D models.

Unit-1
Teaching Hours:9
Introducing Objects
 

Standard primitives, modelling with modifiers, making clones, working with groups.

Unit-1
Teaching Hours:9
Autodesk 3ds Max
 

Introduction, Working, touring the interface, working with objects, and viewing.

Unit-2
Teaching Hours:9
Creating Shapes with Splines
 

Drawing with splines, modifying a shape, outlining and extruding splines, combining and extruding primitive splines, creating a solid form with splines.

Unit-2
Teaching Hours:9
Editing Meshes and Creating and Organizing and Editing Objects
 

Creating shapes with Boolean objects, tracing a sketch, editing meshes, create symmetric forms, smoothing meshes. Naming and renaming objects, organizing objects by layer, lofting an object.

Unit-3
Teaching Hours:9
Light and Shadow
 

Lighting the model, rendering a view, ambient light, adding shadow effects, playing in the shadows, using the light listener, using scene states.

Unit-3
Teaching Hours:9
Enhancing Models with Materials
 

Texture maps, adding materials to object, editing materials, using the standard material, assigning materials to parts of an object.

Unit-4
Teaching Hours:9
Organizing Objects and Scene Management
 

Gaining access, arranging furniture, replacing objects, using the rendered framework window.

Unit-4
Teaching Hours:9
Using the Camera
 

Basics of 3ds max camera, setting up an interior view, creating an environment, using immersive environment for animation, using render type and elements, matching your scene to background image.

Unit-5
Teaching Hours:9
Animation
 

The world of video, Time, creating a quick study animation, key frames, increasing and editing key frames, adding more frames, moving the camera target over time, controlling lights over time.

Text Books And Reference Books:

[1] J. Harper, Mastering Autodesk 3ds Max 2013. Sybex, 2012.

Essential Reading / Recommended Reading

[1] R. L. Derakhshani and D. Derakhshani, Autodesk 3ds Max Essential. Sybex, 2011.

[2] K. L. Murdock, 3ds Max 2012 Bible. Wiley, 2011.

[3] T. Mullen, Introducing Character Animation with Blender. Sybex, 2007.

Evaluation Pattern

CIA: 50%

ESE: 50%

CSC542E - .NET TECHNOLOGY (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The course gives introduction to the .Net framework. It also enable the studentst to learn and develop console, windows and web based application in the .NET framework using C# programming.

Learning Outcome

CO1: Understand the buliding blocks of .Net framework.
CO2: Experiment with C# programming language in .Net framework.
CO3: Design and develop window based applications.
CO4: Develop web based applications for real world problems.

Unit-1
Teaching Hours:10
Introduction
 

Vision and goals of .NET, Building blocks of .Net, Overview of .Net applications, .Net evolution, The .Net Framework Architecture, Intermediate Language(IL), Common Language Runtime (CLR), JIT Compilation, Common Type System(CTS), Common Language System (CLS), Assemblies, IL Disassembler (ILdasm.exe), Namespaces.

C# features

Working  with  methods-  understanding  method  structure,  calling  a  method,  understanding parameter types, overloading methods, virtual methods, overriding methods.

Unit-2
Teaching Hours:10
C# classes
 

Constants,  fields,  methods,  properties,  events,  indexers,  operators,  constructors,  destructors, static modifiers. Compiling with multiple classes, virtual and override methods, abstract methods, sealed classes, Boxing and Unboxing,Working with namespaces, Understanding interfaces, handling exceptions.

Self Learning: Class Inheritance

Unit-3
Teaching Hours:9
Windows Applications
 

Understanding Windows Forms Architecture, Windows controls: Common, Containers, Menus and Tool strips, Data, Reporting. Adding and using windows controls to the form.

Unit-4
Teaching Hours:8
Database programming with ADO.NET
 

Understanding the Dataset classes and their relatives, Understanding OLEDB and SQL Server Support, Understanding common database operations using ADO.NET–Operations that don’t return rows, Data operations that return single row entities, data operations that affect single-row entities, data operations returning sets of rows, data operations affecting sets of rows, operations that return hierarchical data.

Unit-5
Teaching Hours:8
ASP.NET
 

Creating web applications with webforms [ASP.NET], Difference between ASP and ASP.NET, Defining a web application, ASP.NET architecture, ASP.NET webforms, Code behind model, Validation controls in ASP.NET, Server controls and data binding, Grid view, data repeater, data list, Data binding in ASP.NET, Data source controls-sql data source, Data controls–gridview and details view, Login controls.

Text Books And Reference Books:

[1] JeffFerguson, BrianPatterson, Jason Beres ,C# Programming Bible ,Wiley Publishing Inc., Reprint 2006.

Essential Reading / Recommended Reading

[1] JeffProsise, Programming .Net, 2nd Edition, WP Publishers & Distributors Pvt.Ltd, 2009.

[2]  Kevin Hoffman & Jeff Gabriel, Professional .Net Framework, 1stEdition, Wrox PressPublishers,2006.

Evaluation Pattern

CIA - 50%
ESE - 50%

CSC551A - DATA ANALYTICS LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is to help students to do hands-on lab experience by practicing data analytics to get the insights from the chosen area/domain based on the given topics.

Learning Outcome

CO1: Demonstrate the Correlation and Regression methods.

CO2: Design different forecasting models.

CO3: Analyse data classification and clustering based on different methods.

Unit-1
Teaching Hours:30
List of programs
 
  1. Open/ Create a dataset and write all its characteristics. E.g. variable/ attribute, discrete/continuous, scales of measurement, frequency distribution, cumulative distribution, sorting.
  2. Data Preprocessing - Filtering, Data Transformation, Data Integration.
  3. Data Visualization - Graphs and charts.
  4. Data Visualization - Principal Component Analysis, Multi-dimensional Scaling.
  5. Correlation - Linear Correlation, Chi-Square Test.
  6. Regression - Feature Selection.
  7. Forecasting - Finite State Machines, Recurrent Models.
  8. Classification - Any one of the method.
  9. Clustering - Any one of the method.
Text Books And Reference Books:
  1. Thomas A. Runkler, "Data Analystics - Models and Algorithms for Intelligent Data Analysis", Springer Vieweg, 2nd Edition, 2016.
  2. Anil Maheswari, "Data Analytics", McGraw-Hill Education, First Edition, 2017.
Essential Reading / Recommended Reading
  1. Soraya Sedkaoui, "Data Analytics and Big Data", John Wiley & Sons, 2018.
  2. Robert Keane, "Data Analytics: Master the Techniques for Data Science, Big Data and Data Analytics", CreateSpace Independent Publishing Platform, 2017.
  3. Herbert Jones, "Data Analytics: An Essential Beginners Guide to Data Mining, Data Collection, Big Data Analytics for Business, and Business Intelligence Concepts", CreateSpace Independent Publishing Platform, 2018.
Evaluation Pattern

CIA - 50%

ESE - 50%

CSC551B - INTERNET OF THINGS LAB (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To explore students to the world of interconnected devices, communication among these connected devices, transfer of data and further analysis of this data to make appropriate decisions

Learning Outcome

CO1: Analyze the functional blocks involved in Internet of Things.

CO2: Understand the architecture of Internet of Things.

CO3: Infer the process of analysing data in Internet of Things.

CO4: Demonstrate the application of IoT in real world.

Unit-1
Teaching Hours:9
Introduction to Internet of Things
 

Introduction, Definition and Characteristics of IoT, Physical Design  of  IoT,  Things in IoT, IoT Protocols, Logical Design of IoT, IoT Functional Blocks,  IoT Communication Models,  IoT Communications APIs, IoT Enabling Technologies, Wireless Sensor Networks, Cloud Computing, Big Data Analytics, Communication Protocols, Embedded Systems.

Unit-2
Teaching Hours:9
IoT Physical Devices and EndPoints
 

What is an IoT Device, Exemplary Device: Raspberry Pi, About the Board, Linux on  Raspberry Pi, Raspberry Pi interfaces, Programming Raspberry Pi with Python. Other IoT Devices – pcDuino, BeagleBone Black, Cubieboard.

Unit-3
Teaching Hours:9
Domain Specific IoTs and M2M
 

Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry, Health & Lifestyle.

IoT and M2M – Introduction, M2M, Difference between IoT and M2M, SDN and NFV for  IoT.

Unit-4
Teaching Hours:9
Arduino Programming
 

The Arduino Ecosystem, Installing the software, Connecting the Arduino, Opening a sketch, Sketching in code, The Structure of Arduino C, Verifying and Uploading, Working with variables, Making Decisions, Digital Ins and Outs, Analog In, Analog Out.

Unit-5
Teaching Hours:9
Infrastructure and Service Discovery Protocols for the IoT Ecosystem
 

Infrastructure Protocols: Routing Protocol, IEEE 802.15.4, Bluetooth Low Energy, Z-Wave, ZigBee. Protocols for IoT Service Discovery: multicast Domain Name System (mDNS), DNS Service Discovery, Universal Plug and Play. Prominent IoT Service Discovery Products available in the market.

Text Books And Reference Books:

[1]   Arshdeep Bahga and Vijay Madisetti , "Internet of Things: A Hands-on Approach", Universities Press, 2015

   [2]   Pethuru Raj and Anupama C. Raman , “The Internet of Things: Enabling Technologies, Platforms, and Use Cases", CRC Press, 2017.

   [3]  Brian Evans, Beginning Arduino Programming, Apress, 2011

Essential Reading / Recommended Reading

[1]  March Schwartz, “Internet of Things with Arduino Cookbook”, Packt Publishing, 2016

   [2]   Olivier Hersent , David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications and Protocols”, Wiley, 2012.

[3]Peter Waher, “Mastering Internet of Things: Design and create your own IoT applications using Raspberry Pi 3”, Packt Publishing, 2018

Evaluation Pattern

CIA 50%

ESE 50%

CSC551C - DIGITAL IMAGE PROCESSING LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course helps student to understand image enhancement techniques in spatial domain.This also focuses on classification of images using Matlab

Learning Outcome

CO1: Understand the enhancement techniques of images.

CO2: Analyse different filtering methods in Spatial domain.

Unit-1
Teaching Hours:30
List of programs
 

1.     Write a program to display frequency of each pixel occurring in a row of an image.

2.     Write a program to convert color images to Gray scaleImages.

3.     Write a program to perform Rotation of images using differentmethods.

4.     Write a program to perform resizing of images using differentmethods.

5.     Write a program to implement Contraststretching

6.     Write a program to demonstrate smoothening of animage

7.     Write a program to perform non-linear filtering of animage(Median)

8.     Write a program to implement of Edgedetection

9.     Write a program to extract the three color components in theimages

10.  Write a program to perform bit planeslicing.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA 50%

ESE 50%

CSC551D - BUSINESS INTELLIGENCE LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is designed to introduce a concept of Business Intelligence for better business decision. Also gives practical knowledge on implementation of Business Intelligence concepts. 

Learning Outcome

CO1: Explore various modeling techniques and business intelligence methods to various situations using data mining tools.

CO2: Demonstrate the impact of business reporting, information visualization, and dashboards using BI tools.

Unit-1
Teaching Hours:30
Programs
 

1. Practice various data access methods. Representation formats: CSV, FLV, ARFF, XML.

2. Implement data conversion. eg. CSV2ARFF file format conversion in Java.

3. Configuring and testing the ETL tools.

4. Implement pipeline, sampling.

5. Implement surrogate keys and change in dimensions.

6. Practice data source views, dimensions, hierarchies.

7. Implement OLAP explorative data analysis with Pivot Tables.

8. Implement the metrics.

9. Implement Parent-child hierarchies. ROLAP and MOLAP.

10. Implement SQL reporting services. 

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA - 50%

ESE- 50%

CSC552A - UNIX OPERATING SYSTEM LAB (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

UNIX is a multi user and multi-tasking operating system. After learning the concepts of an operating system, it is appropriate to learn how UNIX implements these concepts. The subject is introduced with the features and architecture of UNIX. The file system, Process management and Memory management are discussed to make the students understand the internals of UNIX. Various commands used by UNIX shell is also discussed which makes the users of UNIX comfortable to interact with each other. Bourne shell programming is dealt in depth which can be used to develop applications in UNIX.

Learning Outcome

CO1: Describe the architecture and features of UNIX Operating System and distinguish it from other Operating System

CO2: Apply and change the ownership and file permissions using advance Unix commands.

CO3: Build Regular expression to perform pattern matching using utilities like grep, sed and awk.

CO4: Implement shell scripts for real time applications.

Unit-1
Teaching Hours:8
Introduction to UNIX
 

Evolution of UNIX – UNIX System Structure – Features of Unix - Operating System Services - Unix Kernel - Locating Commands, Internal and External Commands, Flexibility of Command Usage, man: Browsing and Manual Pages On-line, Understanding the man Documentation.

General Purpose Utilities: cal, date, echo, printf, echo, bc, script, passwd, who, uname, tty, stty.

Unit-2
Teaching Hours:9
The UNIX file system
 

The File, I-nodes – Structure of a regular file. The HOME Variable: The Home Directory, Directory related commands: pwd, mkdir, cd, rmdir. Absolute and relative path names. The UNIX File System. File manipulation commands: cat, cp, rm, mv, more, The lp Subsystem: Printing a File, file, wc, Words and Characters, od, The spell and ispell, cmp, comm, diff. File compression commands: gzip, gunzip, tar, zip, unzip. Basic file attributes: The –d Option: Listing Directory Attributes, File Ownership, File Permissions, chmod: Changing File Permissions, Directory Permissions, Changing File Ownership. Hard links, Symbolic Links, ln, umask, and find.

Unit-3
Teaching Hours:9
UNIX process management
 

Process Basics, Process States and Transitions, ps: Process Status, System Processes (-e or –a), Internal and External Commands, Running Jobs in Background, nice: Job Execution With Low Priority, Killing Processes with Signals, Job Control, at and batch: Execute Later, cron: Running Jobs Periodically, time: Timing Processes. PID and PPID.

Unit-4
Teaching Hours:9
Filters and communication simple filters
 

The Sample Database, pr: Paginating Files, head: Displaying the Beginning of a File, tail: Displaying the End of a File, cut: Slitting a File Vertically, paste: Pasting Files, sort: Ordering a File, uniq: Locate Repeated and Non repeated Lines, tr: Translating Characters, An Example: Displaying a Word-count List. Filters using regular expressions: grep: Searching for a Pattern, and egrep. Communication: Communicating with Other Users: Who, Mail, Wall, Send, Mesg, Ftp.

Unit-5
Teaching Hours:10
UNIX shell environment
 

The Wild-cards, Escaping and Quoting, Redirection; Review of vi Operations – Different Modes – Saving and Exiting - Accessing Multiple Files - Interacting with Unix - Miscellaneous Commands - Alphabetical List of Keys. Shell variables - Shell Keywords - Positional parameters - Passing command line arguments. Arithmetic in shell scripts - Read and Echo - Control Structures - if-then-fi - if-then-else-fi - Nested if - Case control structure – Loops - while-until –for - break and continue. Shell meta characters - Exporting variables - User defined Functions.

Text Books And Reference Books:

[1] Sumitabha Das. UNIX Concepts and Applications. 5th Edition, New Delhi: Tata McGraw Hill, 2013.

[2] Yashavant P Kanetkar. Unix Shell Programming. New Delhi: BPB Publications, 2012.

Essential Reading / Recommended Reading

[1] Maurice J Bach. The Design of Unix Operating System. NewDelhi: Prentice Hall of India Pvt. Ltd, 2012.

[2] Paul Love, Joe Merlino, Craig Zimmerman, Jeremy C. Reed, and Paul Weinstein. Beginning UNIX. New Delhi: Wiley Publishing, Inc, (Wrox Publishing) 2007

Evaluation Pattern

CIA - 50%

ESE - 50%

CSC552B - WEB TECHNOLOGY LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The Web Technology Lab provides a great opening for those who want to pursue a career in the web development. Student will learn the core concepts of web site design including the wire framing, planning and hosting. This course will help them to create a interactive website with great look and functionality.

 

Learning Outcome

CO1: Design an interactive website with web tools.

CO2: Design webpages with server-side scripting using PHP.

CO3: Apply web development techniques like CSS and JavaScript for website design.

Unit-1
Teaching Hours:30
List of Programs:
 

 

 

  1. Design/Develop a website that consists of minimum 3 pages (Landing page, Content page and Contact us page) with all the possible tags and information.
  2. Design/Develop an Online Registration page using HTML Forms.
  3. Create a webpage with three different CSS files/ include all type of selector and all 3 types of style sheet.
  4. Create a webpage that applies CSS positioning and formatting.
  5. Design webpages with JavaScript Form Validation.
  6. Design webpages with and Event Handling features.
  7. Design a program that demonstrate PHP-MySQL database connection.
  8. Demonstrate creation, insertion and updation of table using PHP-MySQL.
  9. Design a complete interactive website for portfolio management.
  10. Host the website you have created and share the live website link.

 

Text Books And Reference Books:

[1] Rachel Andrew, Jeremy Keith, “HTML5 for Web Designers”, Second Edition, 2nd Edition, 2016, ISBN: 9781492017899, Publisher - A Book Apart.

Essential Reading / Recommended Reading

[1] Faithe Wempen, Microsoft,” Start Here! Learn HTML5" , 2012

Evaluation Pattern

CIA-50%

ESE-50%

CSC552C - MOBILE APPLICATIONS LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course focuses on developing applications for modern Smartphone operating systems. Most of the course is dedicated to Google's Android. Rapid application development techniques are covered, as well as setup of the development environment, real-world testing, and deployment.

Learning Outcome

Provides solid foundation and skills for Android Programming and other technology applications for Mobile Devices

CO1: Get hands-on experience on most popular application tools

CO2: Create your Mob Apps Portfolio using Android

Unit-1
Teaching Hours:30
List of programs
 

1.      Creating “Hello world” Application.

2.      Creating an Application that displays message based on the screen orientation.

3.      Create an application that displays custom designed Opening Screen.

4.      Create a calculator application using basic views.

5.      Create a Login page for any application.

6.      Create an application to calculate age for a given date of birth using date picker view.

7.      Create an image gallery using image view and select picture from that.

8.      Create menu in Application.

9.      Play an audio, based on the user event.

10.  Read/ write the Local data.

      11.Learn to deploy android Applications

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA: 50%

ESE: 50%

CSC552D - GRAPHICS AND ANIMATION LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To acquire knowledge in three dimensional modelling and animation using  3DS  Max  software and to render the animated scene effectively using light and material design.

Learning Outcome

CO1: Design real-time 3D objects using 3DS Max

CO2: Apply effects to objects using light and material.

CO3: Create animated frames on built models.

Unit-1
Teaching Hours:30
List of programs
 

1.     Modelling basic objects using standard primitives.

2.     Editing shapes with meshes.

3.     Transformations and filling of  images.

4.     Working with  color palette and layers.

5.     Enhancing objects with lights and shadow.

6.     Enhancing models with materials.

7.     Creation of images with special effects.

8.     Rendering a Scene with layers in the time line.

9.     Keyframe animation.

10. Rendering the animation.

Text Books And Reference Books:

[1] J. Harper, Mastering Autodesk 3ds Max 2013. Sybex, 2012.

Essential Reading / Recommended Reading

[1]  R. L. Derakhshani and D. Derakhshani, Autodesk 3ds Max Essential. Sybex, 2011.

[2]  K. L. Murdock, 3ds Max 2012 Bible. Wiley, 2011.

[3]  T. Mullen, Introducing Character Animation with Blender. Sybex, 2007.

Evaluation Pattern

CIA: 50%

ESE: 50%

CSC552E - .NET TECHNOLOGY LAB (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Apply the knowledge acquired on object oriented programming concepts to develop console, window and web based applications.

Learning Outcome

CO1: Design and develop an application using fundamentals of object-oriented programming concepts.
CO2: Demonstrate the skills in designing an interactive Graphical User Interface using windows-based application.

Unit-1
Teaching Hours:30
List of programs
 
  1. To implement output parameter and reference parameter
  2. To implement the concept of indexers
  3. To implement the concept of sealed class
  4. To implement the concept of namespace
  5. To implement the concept of interfaces
  6. To implement the concept of events
  7. To implement exception handling
  8. To designa calculator in windows form
  9. To implement data controls in windows form
  10. To implement validation controls in web form
  11. To implement Data controls in web form
  12. To implement SqlDataReader in ADO.NET
  13. To implement Dataset object in ADO.NET
Text Books And Reference Books:

--

Essential Reading / Recommended Reading

--

Evaluation Pattern

CIA : 50%
ESE : 50%

MAT531 - LINEAR ALGEBRA (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course aims at developing the ability to write the mathematical proofs. It helps the students to understand and appreciate the beauty of the abstract nature of mathematics and also to develop a solid foundation of theoretical mathematics.

Course Objectives : This course will help the learner to

COBJ1. understand the theory of matrices, concepts in vector spaces and Linear Transformations.

COBJ2. gain problems solving skills in solving systems of equations using matrices, finding eigenvalues and eigenvectors, vector spaces and linear transformations.

Learning Outcome

CO1: use properties of matrices to solve systems of equations and explore eigenvectors and eigenvalues.

CO2: understand the concepts of vector space, basis, dimension, and their properties.

CO3: analyse the linear transformations in terms of matrices.

Unit-1
Teaching Hours:15
Matrices and System of linear equations
 

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Unit-2
Teaching Hours:15
Vector Spaces
 

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Unit-3
Teaching Hours:15
Linear Transformations
 

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Text Books And Reference Books:

1. S. Narayan and P.K. Mittal, Text book of Matrices, 10th ed., New Delhi: S Chand and Co. Ltd, 2004.

2. V. Krishnamurthy, V. P. Mainra, and J. L. Arora, An introduction to linear algebra. New Delhi, India: Affiliated East East-West Press Pvt Ltd., 2003.

Essential Reading / Recommended Reading

1. D. C. Lay, Linear Algebra and its Applications, 3rd ed., Indian Reprint, Pearson Education Asia, 2007.

2. S. Lang, Introduction to Linear Algebra, 2nd ed., New York: Springer-Verlag, 2005.

3. S. H. Friedberg, A. Insel, and L. Spence, Linear algebra, 4th ed., Pearson, 2015.

4. Gilbert Strang, Linear Algebra and its Applications, 4th ed., Thomson Brooks/Cole, 2007.

5. K. Hoffmann and R. A. Kunze, Linear algebra, 2nd ed., PHI Learning, 2014.

Evaluation Pattern

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ,

Written Assignment,

Reference work, etc.,

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

 

MAT541A - INTEGRAL TRANSFORMS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course aims at providing a solid foundation upon the fundamental theories on Fourier and Laplace transforms.

Learning Outcome

CO1: Evaluate integrals by using Fourier series and Fourier integrals.

CO2: Apply Fourier sine and cosine transforms for various functions.

CO3: Derive Laplace transforms of different types of functions.

CO4: Utilize the properties of Laplace transforms in solving ordinary differential equations.

Unit-1
Teaching Hours:15
Fourier series and Fourier transform
 

Fourier series and Fourier transform of some common functions. The Fourier integral, complex Fourier transforms, basic properties, transform of the derivative, convolution theorem, and Parseval’s identity. The applications of Fourier transform to ordinary differential equations.

Unit-2
Teaching Hours:15
Fourier sine and cosine transforms
 

Fourier cosine and sine transforms with examples, properties of Fourier Cosine and Sine Transforms, applications of Fourier sine and cosine transforms with examples.

Unit-3
Teaching Hours:15
Laplace transform
 

Laplace Transform of standard functions, Laplace transform of periodic functions, Inverse Laplace transform, solution of ordinary differential equation with constant coefficient using Laplace transform, solution of simultaneous Ordinary differential equations.

Text Books And Reference Books:

B. Davis, Integral transforms and their Applications, 2nd ed., Springer Science and Business Media, 2013.

Essential Reading / Recommended Reading
  1.  E. Kreyszig, Advanced Engineering Mathematics, 18th Ed., New Delhi, India: Wiley Pvt. Ltd., 2010.
  2.  B. S. Grewal, Higher Engineering Mathematics, 39th Ed., Khanna Publishers, July 2005.
  3. P. Dyke, An introduction to Laplace Transforms and Fourier Series, 2nd Ed., Springer Science and Business Media, 2014.
  4. M. D. Raisinghania, Advanced Differential Equations, S Chand and Company Ltd., 2018.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ,

Written Assignment,

Reference work, etc.,

Mastery of the core concepts

Problem-solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT541B - MATHEMATICAL MODELLING (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is concerned with the fundamentals of mathematical modeling. It deals with finding solution to real world problems by transforming into mathematical models using differential equations. The coverage includes mathematical modeling through first order, second order and system of ordinary differential equations.

 

This course will help the learner to

COBJ1.  interpret the real-world problems in the form of first and second order differential equations. 

COBJ2.  familiarize with some classical linear and nonlinear models. 

COBJ3.  analyse the solutions of systems of differential equations by phase portrait method.

Learning Outcome

CO1: Apply differential equations in other branches of sciences, commerce, medicine and others

CO2: Understand the formulation of some classical mathematical models.

CO3: Demonstrate competence with a wide variety of mathematical tools and techniques.

CO4: Build mathematical models of real-world problems.

Unit-1
Teaching Hours:15
Mathematical Modeling through First Ordinary Differential Equations
 

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Unit-2
Teaching Hours:15
Mathematical Modeling through Second Ordinary Differential Equations
 

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

Unit-3
Teaching Hours:15
Mathematical Modeling through system of linear differential equations:
 

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Text Books And Reference Books:
  1. D. G. Zill and W. S. Wright, Advanced Engineering Mathematics, 4th ed., Jones and  Bartlett Publishers, 2010. 
  2. J. R. Brannan and W. E. Boyce, Differential equations with boundary value  problems: modern methods and applications, Wiley, 2011.
Essential Reading / Recommended Reading
  1. C. H. Edwards, D. E. Penney and D. Calvis, Differential equations and boundary value problems: computing and modeling, 3rd ed., Pearson Education Limited, 2010.
  2. D. G. Zill, Differential Equations with Boundary-Value Problems, I7th ed., Cenage learning, 2008.
Evaluation Pattern

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ,

Written Assignment,

Reference work, etc.,

Mastery of the core concepts

Problem-solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT541C - GRAPH THEORY (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course Description:This course is an introductory course to the basic concepts of Graph Theory. This includes definition of graphs, types of graphs, paths and circuits, trees, shortest paths and algorithms to find shortest paths.

Course objectives: This course will help the learner to

COBJ 1: Gain conceptual knowledge on terminologies used in graph theory.

 

COBJ 2: Understand the results on graphs and their properties.

COBJ 3: Gain proof writing and algorithm writing skills.

Learning Outcome

CO1: understand the terminology related to graphs

CO2: analyze the characteristics of graphs by using standard results on graphs

CO3: apply proof techniques and write algorithms

Unit-1
Teaching Hours:15
Introduction to Graphs
 

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Unit-2
Teaching Hours:15
Connectivity
 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Unit-3
Teaching Hours:15
Planarity
 

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Text Books And Reference Books:
  1. G. Chartrand and P. Chang, Introduction to Graph Theory, New Delhi: Tata McGraw Hill, 2006.
Essential Reading / Recommended Reading
  1. N. Deo, Graph Theory with applications to engineering and computer science, Courier Dover Publications, 2017.
  2. J. A. Bondy and U. S. R. Murty, Graph Theory with Applications, Elsevier Science, 1976.
  3. F. Harary, Graph Theory, New Delhi: Narosa, 2001.
  4. D. B. West, Introduction to Graph Theory, New Delhi: Prentice-Hall of India, 2011.
  5. S. A. Choudum, A first Course in Graph Theory, MacMillan Publishers India Ltd, 2013.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment / Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT541D - CALCULUS OF SEVERAL VARIABLES (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course Description: This course aims to enlighten students with the fundamental concepts of vectors, geometry of space, partial differentiation and vector analysis such as gradient, divergence, curl, and the evaluation of line, surface and volume integrals. The three classical theorems, viz., Green’s theorem, Gauss divergence theorem and the Stoke’s theorem are also covered.

Course objectives​: This course will help the learner to

COBJ1. Gain familiarity with the fundamental concepts of vectors geometry of space.

COBJ2. Understand  differential and integral calculus of vector fields.

COBJ3. Demonstrate an understanding of and be able to use Green’s Theorem for the plane, Stokes Theorem, and Gauss’ divergence Theorem to simplify and solve appropriate integrals.

Learning Outcome

CO1: Solve problems involving vector operations.

CO2: Understand the TNB frame work and derive Serret-Frenet formula.

CO3: Compute double integrals and be familiar with change of order of integration.

CO4: Understand the concept of line integrals for vector valued functions.

CO5: Apply Green's Theorem, Divergence Theorem and Stoke's Theorem.

Unit-1
Teaching Hours:15
Vectors and Geometry of Space
 

Fundamentals:Three-dimensional coordination systems, vectors and vector operations, line and planes in space, curves in space and their tangents, integrals of vector functions, arc length in space, curvature and normal vectors of a space, TNB frame, directional derivatives and gradient vectors, divergence and curl of vector valued functions.

Unit-2
Teaching Hours:15
Multiple Integrals
 

Double integrals: Areas, moments, and centres of mass-double integrals in polar form-triple integrals in rectangular coordinates, masses and moments in three dimensions, triple integrals in cylindrical and spherical coordinates, substitutions in multiple integrals.

Unit-3
Teaching Hours:15
Integration in Vector Fields
 

Line integrals, vector fields, work, circulation and flux, path independence, potential functions, and conservative fields, Green’s theorem in the plane, surface area and surface integrals, parametrized surfaces, Stokes’ theorem, the divergence theorem.

Text Books And Reference Books:

J. R. Hass, C Heil, M D Weir, Thomas’ Calculus, 14th ed., USA: Pearson, 2018.

Essential Reading / Recommended Reading
  1. J. Stewart, Multivariable calculus, 7th ed.: Belmont, USA: Brooks/Cole Cengage Learning., 2013.
  2. M. Spivak, Calculus, 3rd ed., Cambridge University Press, 2006.
  3. T. M. Apostol, Mathematical Analysis, 2nd ed., Wiley India Pvt. Ltd., 2011.
  4. S. Lang, Calculus of several variables, 3rd ed., Springer, 2012.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Assignment/problem solving

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT541E - OPERATIONS RESEARCH (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course description: Operations research deals with the problems on optimization or decision making that are affected by certain constraints / restrictions in the environment. This course aims at teaching solution techniques of solving linear programming models, simple queuing model, two-person zero sum games and Network models.

Course objectives: This course will help the learner to

COBJ1. gain an insight executing the algorithms for solving linear programming problems including transportation and assignment problems.

COBJ2. learn about the techniques involved in solving the two person zero sum game.

COBJ3. calculate the estimates that characteristics the queues and perform desired analysis on a network.

Learning Outcome

CO1: On successful completion of the course, the students should be able to solve Linear Programming Problems using Simplex Algorithm, Transportation and Assignment Problems.

CO2: On successful completion of the course, the students should be able to find the estimates that characterizes different types of Queuing Models.

CO3: On successful completion of the course, the students should be able to obtain the solution for two person zero sum games using Linear Programming.

CO4: On successful completion of the course, the students should be able to formulate Maximal Flow Model using Linear Programming and perform computations using PERT and CPM.

Unit-1
Teaching Hours:15
Introduction to Linear Programming Problems
 

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Unit-2
Teaching Hours:15
Queuing Theory and Game Theory
 

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

 

Unit-3
Teaching Hours:15
Network Models
 

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Text Books And Reference Books:

A.H. Taha, Operations research, 9th ed., Pearson Education, 2014.

Essential Reading / Recommended Reading
  1. F.S. Hillier and G.J. Lieberman, Introduction to operations research, 9th Edition, McGraw-Hill, 2009.
  2. Chandrasekhara Rao & Shanthi Lata Mishra, Operations research, Alpha Science International, 2005.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Project

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT551 - LINEAR ALGEBRA USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: This course aims at providing hands on experience in using Python functions to illustrate the notions vector space, linear independence, linear dependence, linear transformation and rank.

Course objectives: This course will help the learner to

COBJ1. The built in functions required to deal with vectors and Linear Transformations.

COBJ2. Python skills to handle vectors using the properties of vector spaces and linear transformations

Learning Outcome

CO1: Use Python functions in applying the notions of matrices and system of equations.

CO2: Use Python functions in applying the problems on vector space.

CO3: Apply python functions to solve the problems on linear transformations.

Unit-1
Teaching Hours:30
Proposed Topics:
 
  1. Operations on matrices
  2. Finding rank of matrices
  3. Reducing a matrix to Echelon form
  4. Inverse of a matrix by different methods
  5. Solving system of equations using various methods
  6. Finding eigenvalues and eigenvectors of a matrix
  7. Expressing a vector as a linear combination of given set of vectors
  8. Linear span, linear independence and linear dependence
  9. Linear transformations and plotting of linear transformations
  10. Applications of Rank-Nullity Theorem
Text Books And Reference Books:
  1. A. Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, no starch press:San Fransisco, 2015.
  2. H P Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer, 2016.
Essential Reading / Recommended Reading
  1. B E Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. C Hill, Learning Scientific Programming with Python, Cambridge University Press, 2016.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT551A - INTEGRAL TRANSFORMS USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course will help students to gain skills in using Python to illustrate Fourier transforms, Laplace transforms for some standard functions and implementing Laplace transforms in solving ordinary differential equations of first and second order with constant coefficient.

Course Objectives​: This course will help the learner to

COBJ 1:code python language using jupyter interface.

COBJ 2:use built in functions required to deal with Fourier and Laplace transforms.

COBJ 3:  calculate Inverse Laplace transforms and the inverse Fourier transforms of standard functions using sympy.integrals

Learning Outcome

CO1.: Acquire skill in Python Programming to illustrate Fourier series, Fourier and Laplace transforms.

CO2.: Use Python program to solve ODE?s by Laplace transforms.

Unit-1
Teaching Hours:30
Integral transforms using Python
 
  1.  Fourier series using sympy and numpy.
  2.  Practical harmonic analysis using math, sympy and numpy.
  3.  Fourier cosine and Fourier sine transforms using sympy and math.
  4.  Discrete Fourier transform using Python.
  5.  Laplace transforms using sympy, sympy.integrals and sympy.abc.
  6.  Inverse Laplace transforms using sympy, sympy.integrals and sympy.abc.
  7. Inverse Fourier transforms using sympy, sympy.integrals and sympy.abc.
Text Books And Reference Books:

J. Nunez-Iglesias, S. van der Walt, and H. Dashnow, Elegant SciPy: The art of scientific Python. O'Reilly Media, 2017. 

Essential Reading / Recommended Reading
  1. J. Unpingco, Python for signal processing. Springer International Pu, 2016.
  2. B. Downey, Think DSP: digital signal processing in Python. O'Reilly, 2016.
  3. M. A. Wood, Python and Matplotlib Essentials for Scientists and Engineers, IOP Publishing Limited, 2015.
Evaluation Pattern

Component

Parameter

Mode of Assessment

Maximum points

CIA I

Mastery of the fundamentals

Lab Assignments

20

CIA-II

Conceptual clarity and software skills

Lab Exam 1

10

Lab Record

Systematic

documentation of Lab exercises

e-Record work

07

Attendance

Regularity and punctuality

Lab Attendance

03

95%-100%-3

90%-94%-2

85%-89%-1

CIA III

Proficiency in executing the commands appropriately

Lab Exam 2

10

Total

50

MAT551B - MATHEMATICAL MODELLING USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: This course provides students with an understanding of the practical and theoretical aspects of mathematical models involving ordinary differential equations (ODEs) using Python programming.

Course objectives:

COBJ1. The course exposes students to various models spanning disciplines such as physics, biology, engineering, and finance.

COBJ2. They will be able to develop a basic understanding of differential equations and skills to implement numerical algorithms to solve mathematical problems using Python.

Learning Outcome

CO1: Acquire proficiency in using Python.

CO2: Demonstrate the use of Python to understand and interpret applications of differential equations

CO3: Apply the theoretical and practical knowledge to real life situations.

Unit-1
Teaching Hours:30
Propopsed Topics
 
  1. Growth of a population – Linear growth, Exponential growth, Logistic growth
  2. Decay Model - Radioactive Decay
  3. Numerical Methods
  4. A Simple Pendulum
  5. Spreading of a Disease
  6. Mixture problems
  7. Trajectory of a ball
  8. Spring mass system
  9. Electrical Circuits
Text Books And Reference Books:
  1. H P Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer, 2016.
  2. H. Fangohr, Introduction to Python for Computational Science and Engineering (A beginner’s guide), University of Southampton, 2015.
Essential Reading / Recommended Reading
  1. B E Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. C Hill, Learning Scientific Programming with Python, Cambridge Univesity Press, 2016.
  3. A. Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, no starch press: San Fransisco, 2015.

 

Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT551C - GRAPH THEORY USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: The course graph theory using Python is aimed at enabling the students to appreciate and understand core concepts of graph theory with the help of technological tools. It is designed with a learner-centric approach wherein the students will understand the concepts of graph theory using programming tools and develop computational skills.

Course objectives: This course will help the learner to

COBJ1. Gain familiarity in Python language using jupyter interface and NetworkX package

COBJ2. Construct graphs and analyze their structural properties.

COBJ3. Implement standard algorithms for shortest paths, minimal spanning trees and graph searching..

Learning Outcome

CO1: construct graphs using related matrices

CO2: compute the graph parameters related to degrees and distances

CO3: gain mastery to deal with optimization problems related to networks

CO4: apply algorithmic approach in solving graph theory problems

Unit-1
Teaching Hours:30
Proposed Topics:
 
  1. Introduction to NetworkX package
  2. Construction of graphs
  3. Degree and distance related parameters
  4. In-built functions for different graph classes
  5. Computation of graph parameters using in-built functions
  6. Graph Operations and Graph Connectivity
  7. Customization of Graphs
  8. Digraphs
  9. Matrices and Algorithms of Graphs
  10. Graph as models.
Text Books And Reference Books:

1. Mohammed Zuhair, Kadry, Seifedine, Al-Taie, Python for Graph and Network Analysis.Springer, 2017.

Essential Reading / Recommended Reading
  1. B. N. Miller and D. L. Ranum, Python programming in context. Jones and Bartlett, 2014.
  2. David Joyner, Minh Van Nguyen, David Phillips. Algorithmic Graph Theory and Sage, Free software foundation, 2008.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT551D - CALCULUS OF SEVERAL VARIABLES USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: The course calculus of several variables using python is aimed at enabling the students to explore and study the calculus with several variables in a detailed manner with the help of the mathematical packages available in Python. This course is designed with a learner-centric approach wherein the students will acquire mastery in understanding multivariate calculus using Python modules.

Course objectives: This course will help the learner to gain a familiarity with

COBJ1. Skills to implement Python language in calculus of several variables

COBJ2. The built-in functions available in library to deal with problems in multivariate calculus

Learning Outcome

CO1: Demonstrate plotting of lines in two and three dimensional space

CO2: implementing appropriate codes for finding tangent vector and gradient vector

CO3: Evaluate line and double integrals using sympy module

CO4: Acquainting suitable commands for problems in applications of line and double integrals.

Unit-1
Teaching Hours:30
Proposed Topics
 
  1. Introduction to basic commands and plotting of graph using matplotlib
  2. Vectors-dot and cross products, plotting lines in two and three-dimensional space, planes and surfaces.
  3. Arc length, curvature and normal vectors.
  4. Curves in sphere: Tangent vectors and velocity- circular helix with velocity vectors.
  5. Functions of two and three variables: graphing numerical functions of two Variables.
  6. Graphing numerical functions in polar coordinates. Partial derivatives and the directional derivative.
  7. The gradient vector and level curves- the tangent plane -the gradient vector field.
  8. Vector fields: Normalized vector fields- two-dimensional plot of the vector field.
  9. Double Integrals: User defined function for calculating double integrals - area properties with double integrals.
  10. Line integrals – Curl and Green’s theorem, divergence theorem.
Text Books And Reference Books:

H P Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer, 2016

Essential Reading / Recommended Reading
  1. B E Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. C Hill, Learning Scientific Programming with Python, Cambridge Univesity Press, 2016.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT551E - OPERATIONS RESEARCH USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: Operations research deals with the problems on optimization or decision making that are affected by certain constraints / restrictions in the environment. This course aims to enhance programming skills in Python to solve problems chosen from Operations Research.

 

Course objectives: This course will help the learner to

COBJ1. gain a familiarity in using Python to solve linear programming problems, calculate the estimates that characteristics the queues and perform desired analysis on a network.

COBJ2. use Python for solving problems on Operations Research.

Learning Outcome

CO1: On successful completion of the course, the students should be able to use Python programming to solve linear programming problems by using simplex method and dual simplex method.

CO2: On successful completion of the course, the students should be able to solve Transportation Problems and Assignment Problems using Python module.

CO3: On successful completion of the course, the students should be able to demonstrate competence in using Python modules to solve M/M/1, M/M/c queues, and Computations on Networks.

Unit-1
Teaching Hours:30
Proposed Topics
 
  1. Simplex method
  2. Dual simplex method
  3. Balanced transportation problem
  4. Unbalanced transportation problem
  5. Assignment problems
  6. (M/M/1) queues
  7. (M/M/c) queues
  8. Shortest path computations in a network
  9. Maximum flow problem
  10. Critical path computations
Text Books And Reference Books:

Garrido José M. Introduction to Computational Models with Python. CRC Press, 2016

Essential Reading / Recommended Reading
  1. A.H. Taha, Operations research, 9th ed., Pearson Education, 2014.
  2. Chinneck, J. W., et al. Operations Research and Cyber-Infrastructure. Springer Science Business Media, LLC, 2009.
  3. Hart, William E. Pyomo: Optimization Modelling in Python. Springer, 2012.
  4. Snyman, Jan A, and Daniel N. Wilke, Practical Mathematical Optimization: Basic Optimization Theory and Gradient-Based Algorithms. Springer., 2018.

 

Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

STA531 - LINEAR REGRESSION MODELS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course deals with simple and multiple linear regression models with their assumptions, estimation and their significance of regression coefficients. Model and variable selection techniques and variable transformation techniques are discussed.

Learning Outcome

CO1: Demonstrate simple and multiple regression analysis with one dependent and one or more independent variables.

CO2: Infer about r-square, adjusted r-square for model selection.

CO3: Apply the concepts of forward, backward and stepwise methods for selecting the independent variables.

CO4: Demonstrate the concepts of heteroscedasticity, multicollinearity, autocorrelation and residual plots.

Unit-1
Teaching Hours:15
Simple Linear Regression
 

Introduction to regression analysis - modelling a response - overview and applications of regression

analysis - major steps in regression analysis - simple linear regression (Two variables): assumptions -

estimation and properties of regression coefficients - significance of regression coefficients.

Unit-2
Teaching Hours:10
Multiple Linear Regression
 

Multiple linear regression model - assumptions - ordinary least square estimation of regression

coefficients - interpretation and properties of regression coefficient - significance of regression

coefficients.

Unit-3
Teaching Hours:10
Criteria for Model Selection and Residual Analysis
 

Mean Square error criteria - R2 and criteria for model selection - Forward, Backward and Stepwise procedures - Statistical analysis of residuals - various types of residuals - residual plots, Need of the transformation of variables - Box-Cox transformation.

Unit-4
Teaching Hours:10
Tests of assumptions in MLR
 

Concept of heteroscedasticity - multicollinearity - autocorrelation and their practical consequences -

detection and remedial measures.

Text Books And Reference Books:

1. Montgomery D.C, Peck E.A and Vining G.G, Introduction to Linear Regression Analysis, 5th

edition, John Wiley and Sons Inc., New York, 2012.

2. Debasis Sengupta and S. R Jammalamadaka, Linear Models and Regression with R: An

Integrated Approach, World Scientific Publishing, Singapore, 2020

Essential Reading / Recommended Reading

1. George A.F.S. and Lee A.J., Linear Regression Analysis, John Wiley and Sons, Inc, 2012.

 

2. Pardoe I, Applied Regression Modeling, John Wiley and Sons Inc, New York, 2012

 

3. Wasserman L, All of Statistics - A Concise Course in Statistical Inference, Springer Series in

Statistics, 2010.

Evaluation Pattern

CIA 50%

ESE 50%

STA541A - SAMPLING TECHNIQUES (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

This course designed to introduce about official statistical system in India and to understand the concepts of basic Sample survey designs.

Learning Outcome

 After completion of this course the students will be able to

1. Demonstrate the basic principles and different steps in planning a sample survey.

2. Analysis various sampling techniques and their application

3. Demonstrate the official Statistical System in India.

Unit-1
Teaching Hours:10
Introduction to Sampling Theory
 

Concepts of population and sample. Complete enumeration vs. sampling. Planning of Sampling Survey. Types of sampling: non-probability and probability sampling, basic principle of sample survey,  population mean, total and proportion, variances of these estimates and sample size determination, Sampling and non-sampling errors, determination of sample size.

Unit-2
Teaching Hours:10
Simple Random Sampling
 

Simple Random Sampling: Probability of selecting any specified unit in the sample, selection of simple random sample, simple random sample from population with given frequency distribution, SRS of attribute, size of simple random sample for specified precision. Concept of SRSWOR and SRSWR.

Unit-3
Teaching Hours:15
Stratified Random Sampling and Systematic Sampling
 

Stratified random sampling: Technique, estimates of population mean and total, variances of these estimates. Systematic Sampling: Technique, estimates of population mean and total, variances of these estimates (N=nxk).Comparison of systematic sampling with SRS and stratified sampling.

Unit-4
Teaching Hours:10
Official Statistical System
 

Present Official Statistical System in India relating to census of population, agriculture, industrial production, and prices; methods of collection of official statistics, their reliability and limitation and the principal publications containing such statistics. Also the various agencies responsible for the data collection- C.S.O., N.S.S.O., Office of Registrar General, their historical development, main functions and important publications.

Text Books And Reference Books:

1.    1.  Cochran W.G, Sampling Techniques, 3rd Edition, John Wiley and Sons, New York, 2008.

2.     2. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, India 2009.

Essential Reading / Recommended Reading

1

  1. Mukhopadhyay P, Theory and Methods of Survey Sampling, 2nd Revised edition, PHI Learning New Delhi, 2008.

  2. Arnab R, Survey Sampling Theory and Applications, Academic Press, UK, 2017.

  3. Goon A.M., Gupta M.K. and Dasgupta B., Fundamentals of Statistics (Vol.2), World Press 2016.

  4. Guide to current Indian Official Statistics, Central Statistical Office, GOI, New Delhi.

4.      Guide to current Indian Official Statistics, Central Statistical Office, GOI, New Delhi.

Evaluation Pattern

CIA 50%

ESE 50%

STA541B - DESIGN OF EXPERIMENTS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course introduces various experimental designs, selection of appropriate designs in planning a scientific experimentation.

Learning Outcome

CO1: CO1 Demonstrate the concepts of Analysis of Variance with comparison of more than two treatment.

CO2: Apply the concepts of ANCOVA to compare the efficiency of various designs.

CO3: Demonstrate the applications of factorial experiments with confounding.

Unit-1
Teaching Hours:10
Analysis of variance
 

Meaning and assumptions. Fixed, random and mixed effect models. Analysis of variance of one-way and

two-way classified data with and without interaction effects. Multiple comparison tests: Tukey’s method,

critical difference.

Unit-2
Teaching Hours:10
Experimental designs
 

Principles of design of experiments. Completely randomized, randomized block, and Latin square designs

(CRD, RBD, and LSD) -layout formation and the analysis using fixed effect models.

Unit-3
Teaching Hours:10
Efficiency of a design and missing plot technique
 

Comparison of efficiencies of CRD, RBD, and LSD. Estimation of single missing observation in RBD and

LSD and analysis.

Unit-4
Teaching Hours:15
Factorial experiment
 

Factorial experiment: Basic concepts, main effects, interactions, and orthogonal contrasts in 2and 2factorial experiments. Yates’ method of computing factorial effects total. Analysis and testing thesignificance of effects in 2and 2factorial experiments in RBD. Need for confounding. Complete and partial confounding in a 2factorial experiment in RBD - layout and its analysis.

Text Books And Reference Books:

1. Montgomery D.C, Design and Analysis of Experiments, 10th edition, John Wiley and Sons Inc.,

New York, 2019.

2. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th edition (Reprint), Sultan

Chand and Sons, India, 2019.

Essential Reading / Recommended Reading

1. Mukhopadhyay P, Mathematical Statistics, 2nd edition revised reprint, Books and Allied (P) Ltd,

2016.

2. Lawson J, Design and Analysis of Experiments with R, 1st edition, CRC Press, 2015.

Evaluation Pattern

CIA 50%

ESE 50%

STA541C - ACTUARIAL STATISTICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is designed to introduce the application of statistical methods in framing the insurance

policies.

Learning Outcome

CO1: Demonstrate the principle terms used and major life insurance covered by Indian life insurance.

CO2: Infer the calculation of premium for various life insurance policies.

Unit-1
Teaching Hours:10
Introductory Statistics and Insurance Applications
 

Discrete, continuous and mixed probability distributions. Insurance applications, sum of random variables.

Utility theory: Utility functions, expected utility criterion, types of utility function, insurance and utility

theory.

Unit-2
Teaching Hours:10
Principles of Premium Calculation
 

Properties of premium principles, examples of premium principles. Individual risk models: models for

individual claims, the sum of independent claims, approximations and their applications.

Unit-3
Teaching Hours:10
Survival Distribution and Life Tables
 

Uncertainty of age at death, survival function, time until death for a person, curate future lifetime, force of

mortality, life tables with examples, deterministic survivorship group, life table characteristics,

assumptions for fractional age, some analytical laws of mortality.

Unit-4
Teaching Hours:15
Life Insurance
 

Models for insurance payable at the moment of death, insurance payable at the end of the year of death

and their relationships. Life annuities: continuous life annuities, discrete life annuities, life annuities with

periodic payments. Premiums: continuous and discrete premiums.

Text Books And Reference Books:

1. Corazza M, Legros F, Perna C and Sibillo M, Mathematical and Statistical Method for Actuarial

Science and Finance, Springer, 2017.

2. Dickson C.M.D, Insurance Risk and Ruin, International Series on Actuarial Science, Cambridge

University Press, 2016.

Essential Reading / Recommended Reading

1. CT-5 General Insurance, Life and health contingencies, Institute of Actuaries of India.

2. Mishra M.N and Mishra S.B, Insurance: Principles and Practice, 22nd edition, S. Chand

Publications, 2016.

3. IC-02 (Revised), Practice of Life assurance, Insurance Institute of India.

Evaluation Pattern

CIA 50%

ESE 50%

STA541D - INTRODUCTION TO SPATIAL STATISTICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

This course designed as an application of statistics in geographical data analysis

Learning Outcome

CO1: Demonstrate the basic biological concepts in genetics

CO2: Infer the bioassays and their types

CO3: Demonstrate the Feller?s theorem and dose response estimation using regression models and dose allocation schemes.

Unit-1
Teaching Hours:15
Introduction
 

Spatial Statistics, Geostatistics, Spatial Autocorrelation, Important properties of MC, Relationships between MC and GR, join count statistics, Graphic portrayals: the Moran scatterplot and the semi-variogram plot, Impacts of spatial autocorrelation, Testing for spatial autocorrelation in regression residuals.

Unit-2
Teaching Hours:10
Spatial Sampling
 

Puerto Rico DEM data, Properties of the selected sampling design, Sampling simulation experiments on a unit square landscape, sampling simulation experiments on a hexagonal landscape structure, Spatial autocorrelation and effective sample size.

Unit-3
Teaching Hours:10
Spatial Composition and Configuration
 

Spatial heterogeneity, ANOVA, Testing for heterogeneity over a plan, regional supra-partitionings, direction supra-partitionings, Spatial weight metrics, Spatial heterogeneity.

Unit-4
Teaching Hours:10
Spatial Regression
 

Linear regression, non-linear regression, Binomial/logistic regression, Poisson/negative binomial regression, simple kriging, universal kriging, simulated experiments.

Text Books And Reference Books:

1.      Yongan C, Griffith D.A, Spatial Statistics & Geostatistics: Theory and Applications for Geographic Information Science & Technology, Sage Publication, 2013.

 

2.      Carlo G, Xavier G, Spatial Statistics and Modeling, Springer, 2010.

 

Essential Reading / Recommended Reading

1.      Van Lieshout M.N.M, Theory of Spatial Statistics: A Concise Introduction, CRC Press, 2019.

      2. Kalkhan M.A, Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping, CRC Press, 2011.

Evaluation Pattern

CIA 50%

ESE 50%

STA551 - LINEAR REGRESSION MODELS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a practical exposure to the students in Simple and Multiple linear

Regression Analysis.

Learning Outcome

CO1: Demonstrate the fitting of linear regression models for the real time data

CO2: Infer model adequacy through various model selection process.

Unit-1
Teaching Hours:30
Practical assignments using R programming
 

1. Scatter Plots diagnosis.

2. Estimation of simple regression model.

3. Significance of simple linear regression.

4. Confidence Interval Estimation of simple linear regression.

5. Estimation of Multiple regression model.

6. Variable selection in multiple regression

7. Significance of multiple linear Regression.

8. Confidence interval for multiple linear Regression.

9. Residuals Plots, detection of outliers and their interpretation in simple and multiple linear

regression.

10. Checking for Normality of Residuals.

11. Checking for Multicollinearity in simple and multiple linear regression.

12. Checking for Heteroscedasticity and auto-correlation in simple and multiple linear regression

Text Books And Reference Books:

Seema Acharya, Data Analytics Using R, CRC Press, Taylor & Francis Group, 2018.

Essential Reading / Recommended Reading

Pardoe I, Applied Regression Modeling, John Wiley and Sons Inc, New York, 2012

Evaluation Pattern

CIA 50%

ESE 50%

STA552A - SAMPLING TECHNIQUES PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

The course is designed to provide a practical exposure to the students in application of different sampling techniques.

Learning Outcome

CO1: After completion of this course the students will acquire the knowledge on different sampling techniques

CO2: After completion of this course the students will able to decide the application of different sampling techniques under different situation.

CO3: After completion of this course the students will be able to design sampling procedures for various situations

Unit-1
Teaching Hours:30
Practical Assignments using EXCEL/R:
 

1.      Random sampling using Random number tables.

2.      Concepts of unbiasedness, Variance, Mean square error etc.

3.      Exercise on Simple Random Sampling with Replacement.

4.      Exercise on Simple Random Sampling without Replacement.

5.      Concepts of Simple Random Sampling for Attributes.

6.      Exercise on Stratified Sampling.

7.      Efficiency of stratified sampling over SRSWR and SRSWOR

8.      Estimation of gain in precision due to stratification.

9.      Exercise on Systematic sampling.

10.  Efficiency of Systematic sampling over SRSWR and SRSWOR

Text Books And Reference Books:

1.      Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, India 2009.

Essential Reading / Recommended Reading

1.      Arnab R, Survey Sampling Theory and Applications, Academic Press, UK, 2017.

Evaluation Pattern

 

CIA-50%
ESE-50%

STA552B - DESIGN OF EXPERIMENTS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a practical exposure to the students for the various experimental

designs.

Learning Outcome

CO1: Demonstrate the construction and analyses of various experimental designs using R programming.

CO2: Demonstrate the efficiencies of various designs using R

Unit-1
Teaching Hours:30
Practical assignments using R programming
 

1. Construction of ANOVA for one way classification

2. Construction of ANOVA for two way classification

3. Analysis of CRD

4. Analysis of RBD

5. Efficiency of RBD over CRD

6. Analysis of LSD

7. Efficiency of LSD over RBD

8. Efficiency of LSD over CRD

9. Analysis of 22 factorial experimental using RBD layout

10. Analysis of 23 factorial experimental using RBD layout

11. Analysis of 23 factorial experimental using RBD layout (Complete confounding)

12. Analysis of 23 factorial experimental using RBD layout (Partial confounding)

Text Books And Reference Books:

1. Seema Acharya, Data Analytics Using R, CRC Press, Taylor & Francis Group, 2018.

Essential Reading / Recommended Reading

1. Lawson J, Design and Analysis of Experiments with R, CRC Press, 2015.

Evaluation Pattern

CIA 50%

ESE 50%

STA552C - ACTUARIAL STATISTICS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a practical exposure to the students in Actuarial Modeling.

Learning Outcome

CO1: To develop a deeper understanding of the premium and risk calculations of life insurance policies.

CO2: To implement actuarial statistics in real life

CO3: To construct new models using real-life concepts

Unit-1
Teaching Hours:30
Practical assignments using EXCEL:
 

 

  1. Premium calculation
  2. Risk computation for different utility models

  3. Discrete and continuous risk calculations

  4. Calculation of aggregate claims for collective risks

  5. Calculation of aggregate claim for individual risks

  6. Computing Ruin probabilities and aggregate losses

  7. Annuity and present value of the contract

  8. Computing premium for different insurance schemes

  9. Practical based on life models and tables

Text Books And Reference Books:
  1. Corazza M, Legros F, Perna C and Sibillo M, Mathematical and Statistical Method for Actuarial Science and Finance, Springer, 2017.

  2. Dickson C.M.D, Insurance Risk and Ruin, International Series on Actuarial Science, 2nd edition, Cambridge University Press, 2016.

Essential Reading / Recommended Reading
  1. CT-5 General Insurance, Life and health contingencies, Institute of Actuaries of India.  

  2. Mishra M.N and Mishra S.B, Insurance: Principles and Practice, 22nd edition, S. Chand Publications, 2016.

  3. IC-02 (Revised), Practice of Life assurance, Insurance Institute of India.

Evaluation Pattern

 

CIA 50%
ESE 50%

STA552D - SPATIAL STATISTICS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

This course is designed to teach practical Spatial problems using statistical softwares.

Learning Outcome

CO1: Demonstrate practically evaluate Spatial Statistical models using R programming.

Unit-1
Teaching Hours:30
Practical assignments using R programming:
 

 

1.     Moran scatter plot

2.      Semi-variogram plot

3.      Estimation of Spatial Autocorrelation

4.      Testing for spatial autocorrelation in regression residuals

5.      Sampling simulation experiments on a unit square landscape

6.      Sampling simulation experiments on a hexagonal landscape structure

7.      Calculation of effective sample size

8.      Spatial heterogeneity

9.      Testing for heterogeneity over a plan: regional supra-partitionings

10.  Testing for heterogeneity over a plan, direction supra-partitionings

11.  Spatial Linear regression

12.  Spatial Non-linear regression

Text Books And Reference Books:

1.   Yongan C, Griffith D.A, Spatial Statistics & Geostatistics: Theory and Applications for Geographic Information Science & Technology, Sage Publication, 2013.

2.      Carlo G, Xavier G, Spatial Statistics and Modelling, Springer, 2010.

Essential Reading / Recommended Reading

1. Van Lieshout M.N.M, Theory of Spatial Statistics: A Concise Introduction, CRC Press, 2019.

2. Kalkhan M.A, Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping, CRC Press, 2011.

Evaluation Pattern

 

CIA 50%
ESE 50%

CSC631 - DESIGN AND ANALYSIS OF ALGORITHMS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Understand the concept of a design and develop algorithm, mathematical aspects and analysis of algorithm, sort and search algorithms, various algorithmic techniques, and design methods. 

Learning Outcome

Course Outcomes           

CO1:Demonstrate their ability to apply appropriate Data Structures.

CO2: Design and develop algorithms using relevant data structure operations.

CO3: Evaluate the Algorithms for its efficiency.

Unit-1
Teaching Hours:7
Introduction
 

Algorithm-definition, Specification- pseudo code conventions, recursive algorithms, Performance analysis – space complexity, time complexity, asymptotic notation, practical complexities, performance measurement, Randomized algorithms- basics of probability theory, identifying the repeated element, primality testing, advantages and disadvantages.

Unit-2
Teaching Hours:8
Elementary Data Structures
 

Stacks and queues, Trees- terminology, binary trees, Dictionaries- binary search trees, cost amortization, Priority queues- heaps, heap sort, Sets and disjoint Set Union-union and find operations, Graphs-definitions, representations.

Unit-3
Teaching Hours:8
Divide and Conquer
 

General method, Binary search, Finding the maximum and minimum, Merge sort, quick sort-performance measurement

Unit-4
Teaching Hours:12
Greedy Method & Dynamic Programming
 

The general method, Knapsack problem, Minimum cost spanning trees- Prim’s algorithm, Kruskal’s algorithm, Single-source shortest paths, Dynamic Programming: The general method, Multistage graphs, All pairs shortest paths, - -optimal binary search trees - The traveling salesperson problem.

Unit-5
Teaching Hours:10
Backtracking & Branch And Bound
 

Backtracking- The general method, The 8-queens problem, sum of subsets, graph coloring Hamiltonian cycles. , Branch and Bound: Least cost search, Bounding, FIFO Branch and bound, LC branch and bound, Knapsack problem, Traveling salesperson problem.

Text Books And Reference Books:

[1] Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekaran, Fundamentals of computer algorithms, Galgotia Publications, 2007.

Essential Reading / Recommended Reading

 [1] Sara Baase and Allen VanGelder, Computer Algorithms Introduction to design and Analysis, Third edition, Pearson education, 2004.

Evaluation Pattern

 CIA  - 50%

ESE - 50%

CSC641A - INTRODUCTION TO SOFT COMPUTING (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The main objective of this course is to provide fundamental knowledge of soft computing techniques.On successful completion of the course,students will acquire fundamental knowledge of  artificial neural network, fuzzy Logic and genetic algorithms.

Learning Outcome

CO1:Describe  the structure of artificial neural network and Biological neural network.

CO2:Demonstrate various artificial neural network models,supervised,unsupervised and reinforcement learning methods.

CO3:Apply Perceptron (Single and Multiple output classes) and Back propagation algorithm in real time applications.

 

Unit-1
Teaching Hours:9
Introduction to Soft Computing
 

 Neural Networks-Application Scope of Neural Networks-Fuzzy Logic-Genetic Algorithm-Soft Computing.

Introduction to Artificial Neural Networks

Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network.

 

Unit-2
Teaching Hours:9
Basic Models of ANN
 

 Connections-Learning-Supervised Learning-Unsupervised Learning-Reinforcement Learning-Activation Functions

Important Terminologies of ANN- Weights, Bias, Threshold, Learning Rate, Momentum Factor, Vigilance Parameter, Notations.

Unit-3
Teaching Hours:9
Supervised Learning Network
 

 Perceptron Networks-Theory-Perceptron Learning Rule-Architecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.

Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.

Unit-4
Teaching Hours:9
Introduction to Fuzzy Logic and Sets
 

 Introduction to Fuzzy Logic - Fuzzy Sets – Fuzzy set operations- properties of Fuzzy sets.

Fuzzy Relations: cardinality-operations and properties of fuzzy relations-fuzzy composition.

Fuzzy membership functions -Features of membership functions- Fuzzification- Methods of Membership value assignments.

Unit-5
Teaching Hours:9
Genetic Algorithm
 

 Introduction to Genetic Algorithm-Biological Background-Genetic Algorithm and Search Space-Genetic Algorithm vs Traditional Algorithms-Basic Terminologies in Genetic Algorithm-Simple GA-General Genetic Algorithm

Text Books And Reference Books:

[1] S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018.

[2]S.N.Sivanandam,S. Sumathi, S.N.Deepa, Introduction to Neural Networks using MATLAB 6.0,Tata McGraw-Hill, New Delhi, 2010.

Essential Reading / Recommended Reading

[1] Satish Kumar, Neural Networks – A Classroom approach, Tata McGraw-Hill, New Delhi,2007.

[2] Martin T. Hagan, Howard B. Demuth, Mark Beale, Neural Network Design, Thomson Learning, India, 2002.

[3] Simon Haykin, Neural Networks, PHI,2nd Edition,2005.

[4] Ethem Alpaydin, Introduction To Machine Learning, PHI, 2005.

[5]  J.S.R. Jang, C.T.Sun, E.Mizutani, Neuro-Fuzzy and Soft Computing, PHI, 2012.

Evaluation Pattern

CIA – 50 %

 ESE - 50 % 

 

CSC641B - CLOUD COMPUTING (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course covers a series of current cloud computing technologies,  including  technologies for Infrastructure as a Service, Platform as a Service, Software as a Service, and Physical Systems as a Service. For different layers of the cloud  technologies, practical  solutions such  as Google, Amazon, Microsoft,SalesForce.com.

Learning Outcome

CO1: Demonstrate the fundamental principles of distributed computing.

CO2: Apply distributed computing in Cloud Computing.

CO3: Evaluate the business models that underlie Cloud Computing.

Unit-1
Teaching Hours:7
Introduction
 

The vision of cloud computing - Characteristics and benefits - Challenges ahead - Historical developments - Distributed systems - Virtualization -  Building  cloud  computing  environments - Application development - Infrastructure and system development  -  Computing platforms andtechnologies.

Unit-2
Teaching Hours:10
Principles of Parallel computing and Virtualization
 

Principles of Parallel Computing – Parallel vs. distributed computing - Elements of parallel computing - Hardware architectures for parallel processing Approaches to parallel programming - Laws of caution. 

Introduction to virtualization - Characteristics of virtualized environments - Taxonomy of virtualization techniques – Hardware Virtualization - Virtualization  and cloud computing -  Pros and cons of virtualization.

Unit-3
Teaching Hours:9
Cloud Computing Architecture
 

The Cloud reference model – Architecture – Types of Cloud – Public Cloud – Private Cloud – Hybrid Cloud – Community Cloud – Economies of the cloud. 

Unit-4
Teaching Hours:10
Cloud Platforms in Industry
 

Amazon web services: Compute services - Storage services - Communication services - Additional services. Google AppEngine: Architecture and core concepts - Application life  cycle - Cost model – Observations. Microsoft azure: Azure core concepts - SQL azure - Windows azure platform appliance.

Unit-5
Teaching Hours:9
Data in the cloud and Cloud Applications
 

Data in the cloud: Relational databases - Cloud file systems: GFS and HDFS - BigTable, HBase - Cloud data stores: Datastore and SimpleDB 

Cloud Application: Healthcare: ECG analysis in the cloud - Biology: protein structure prediction - Biology: gene expression data analysis for cancer diagnosis  -  Geoscience:  satellite image processing.

 

Text Books And Reference Books:

[1] RajkumarBuyya, Christian Vecchiola and S. ThamaraiSelvi ―Mastering Cloud Computing” - Foundations and Applications Programming , MK publications, 2013.

Essential Reading / Recommended Reading

[1] Michael J.Kavis, “Architecting the Cloud: Design  Decisions  for  Cloud  Computing Service Models SaaS, PaaS, and IaaS‖, John Wiley & Sons Inc., Jan 2014.

 

 

Evaluation Pattern

CIA: 50%

ESE: 50%  

CSC641C - COMPUTER ARCHITECTURE (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course deals with concepts and models of computer peripherals. It explains a set of disciplines that describes a computer system by specifying its parts and their relations. The course provides insights into the basic design of an ALU, the memory design, the various operations performed.

Learning Outcome

CO1: Understand the evolution of computer hardware to meet the needs of multi-processing systems.

CO2: Demonstrate the basic computer organization & design and state the significant components in CPU.

CO3: Implement computer arithmetic algorithms and explain the input-output organization.

Unit-1
Teaching Hours:7
Introduction
 

Basic Model of a Computer, Computer Components, Register transfer and Micro operations: Register Transfer Language ,Register Transfer , Bus and Memory Transfers,  Arithmetic  Micro operations , Logic Micro operations , Shift Micro operations, Arithmetic Logic and  ShiftUnit.

Unit-2
Teaching Hours:9
Basic Computer organization and design
 

Instruction codes, Computer registers, Computer Instruction, Timing and control, Instruction cycle, Memory reference instructions, Input output and Interrupt, Design of basic computer, Design of Accumulator logic.

Unit-3
Teaching Hours:9
Central Processing Unit
 

 

Introduction, General Register Organization, Stacks organizations-Register stack, Memory stack, Instruction formats- Three address instruction, two address instruction, one address instruction, zero address instruction , Addressing modes, Data transfer  and  manipulation- Data transfer instructions, Data manipulationinstructions.

Unit-4
Teaching Hours:10
Computer Arithmetic
 

 

Introduction ,Addition and Subtraction – Addition and subtraction with signed magnitude  data, addition and subtraction with signed 2’s complement data ,Multiplication Algorithms- Signed magnitude ,Booth multiplication algorithm, array multiplier ,Division Algorithms- signed magnitudealgorithm.

Unit-5
Teaching Hours:10
Input Output Organization
 

 

Peripheral Device, Input Output Interface – I/O bus and interface modules, I/O  versus  memory bus, Asynchronous data transfer, Modes of transfer – programmed I/O , Interrupt initiated I/O, , Direct Memory Access – DMA controller and DMAtransfer.

 

 

Text Books And Reference Books:

[1]   Mano M Morris, Computer System Architecture, PHI, 3rd Edition,2008.

Essential Reading / Recommended Reading

[1]  Stalling, Williams. Computer Organization and Architecture, 7th Edition, 2010.

[2]   Hayes, John P, Computer Architecture and Organization, 3rd Edition, McGraw-Hill, 2008.

Evaluation Pattern

CIA-50%

ESE-50%

CSC641D - OOAD USING UML (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The course provides instruction and practical experience focusing on the effective use of object-oriented methodology life cycle models and the judicious use of software modelling as applied to a software development process.

Learning Outcome

CO1: Understand the object oriented life cycle.

CO2: Know how to identify classes, objects, relationships.

CO3: Learn the Object Oriented Design process.

CO4: Understand about software quality and usability.

CO5: Build model use case diagrams.

Unit-1
Teaching Hours:12
Complexity
 

The inherent complexity of software, The Structure of complex systems, Bringing order to chaos, on designing complex systems, Categories of analysis and Design methods.

The Object Model

The evolution of object model, Elements of object model.

Unit-2
Teaching Hours:10
Classes and Objects
 

The Nature of an Object, Relationship among objects, nature of a class, Relationship among classes.

Unit-3
Teaching Hours:8
Introduction to Modeling and UML
 

Importance of modeling, principles of modeling, object oriented modeling, overview of UML conceptual model of the UML, Architecture.

Unit-4
Teaching Hours:10
Basic Structural Modeling
 

Common Mechanism: Terms and Concepts, Common modeling techniques. Diagrams, Class Diagrams.

Advanced Structural Modeling

Interfaces, Types and Roles, Packages, Object Diagrams.

Unit-5
Teaching Hours:10
Basic Behavioral Modeling
 

Interaction, Interaction diagram, Use case, Use case diagram.

Advanced BehavioralModeling

State Machines, State Diagrams.

Unit-6
Teaching Hours:10
Architectural Modeling
 

Deployment, Deployment Diagram, Collaboration

Case Study

Any application can be discussed with help of an open tool.

Text Books And Reference Books:

[1] Michael Blaha,JamesRumbaugh, Object Oriented Modeling and Design with UML, 2nd Edition, Pearson, 2010.

Essential Reading / Recommended Reading

[1] Grady Booch, Robert A.Makimchul,MichaelW.EagelJimConallen,Kelli A. Houston, Object Oriented Analysis and Design with Applications, 3rd Edition, Pearson Education Inc,2013.

[2] Grady Booch, James Rumbaugh, Ivar Jacobson, The Unified Modeling Language User Guide, 2nd Edition, Pearson Education Inc,2013.

Evaluation Pattern

CIA - 50%

ESE - 50%

CSC641E - USER EXPERIENCE DESIGN(UX) (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The UI/UX course provides a great entry point for those who want to pursue a career in the  user interface design and development. Student will learn the core principles of visual design, including building storyboards, choosing color schemes and visualizing the  ideal  user  interface to improve the user experience. This course will help to create intuitive and great- looking software products that users will love, and boost company’s ability to persuade audiences into becomingbuyers.

Learning Outcome

CO1: Describe design principles.

CO2: Demonstrate impactful visual design and color concepts. CO3: Apply design principles and skills for design prototype. CO4: Design an intuitive design for software products.

Unit-1
Teaching Hours:9
Introduction
 

HCI-Human computer Interaction-Fundamentals of Design-people and design-Visual Design-overview -difference between visual & UI/UX, UI design trends, Roles of a UI designer, UI UX process-UX- UX terminologies-elements-layers-roles-user centered vs. value-centered design-usertypes.

Unit-2
Teaching Hours:9
Principles
 

Visual Communication-Design principles-Design elements-Color theory-Typography

Unit-3
Teaching Hours:9
User Experience (UX)
 

What makes an experience-the cost of overlooking your users-a balanced approach to solving problems-involving users to perfect your product-good and bad user experiences-Understand the business problem- understand the user context- making sense of what you have found- prototype the solution –test learn tweak.Iterate.

Unit-4
Teaching Hours:9
Designing for Voice User Interfaces
 

Introduction-History-what is VUI designer?-chat bots-Basic Voice user interface design principles-designing for mobile devices verses IVR systems-conventional design-error handling-personas, avtars, actor and video games-Speech Recognition Technology-Advanced Voice User Interface Design-User testing. Hands on reference Amazon Alexa, Google Dialogflow

Unit-5
Teaching Hours:9
Case Study / Tools / Design Lab
 

Case study based on domain-web-mobile-product interaction-software tools-mockups- interactive design. Learn through cheat-sheets- Invision-AdobeXD-Sketch-UXPin-FluidUI- Portfolio creation through behance.net

Text Books And Reference Books:

[1]  DonalsChesnut,KevinPNichols,“UXforDummies”,JohnwileyandSons,2014

[2]     Jodie Moule, “KILLER UX Design”, Site point , Shroff Publishers, 2015 ISBN: 978:93:5213:175-4

[3]   CathyPearl, “Designing Voice User Interfaces”, O’Reilly Media Inc, 2017, ISBN : 978- 93-5213-526-4

Essential Reading / Recommended Reading

[1]  DonaldA.Norman,BasicBooks,"TheDesignofEverydayThings",Inc.NewYork,NY,

USA ©2002 ISBN: 9780465067107

[2]   Krug, Steve, Don't Make Me Think, Revisited : a Common Sense Approach to Web Usability”, [Berkeley, Calif.] : New Riders, 2014.Print

[3]   William lidwell, Kritina Holden,Jill Butler, “Universal Principles of Design”, Rockport Publishers, 2010, ISBN-13: 978-1-592453-587-3,ISBN-10:1-59253-587-9.

Evaluation Pattern

CIA : 50

ESE : 50

CSC681 - MAIN PROJECT (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The main aim of this course is to develop practical knowledge of the students on building a project using any of their interested concepts. Students identifies real world problem, design and develop the project.

Learning Outcome

CO1: Identify the problem and understand the practical concepts to develop project

CO2: Analyse the problem to find the solutions as per the requirement.

CO3: Create a working project that satisfies the need of the end user.

Unit-1
Teaching Hours:60
MAIN PROJECT
 

This main project helps the student to apply the concepts which they have learnt  in  the previous semesters. Students can use any modern technology or tool for their project. Student has to identify and understand the real world problems in consultation with the guide to select the project. Students will be divided into batches, each batch containing not more than 3 students.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA:50%

ESE:50%

MAT631 - COMPLEX ANALYSIS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course description: This course enables the students to understand the basic theory and principles of complex analysis.

COBJ1.     understand the theory and geometry of complex numbers.

COBJ2.     evaluate derivatives and integrals of functions of complex variables.

COBJ3.     examine the transformation of functions of complex variables.

COBJ4.   obtain the power series expansion of a complex valued function.

Learning Outcome

CO 1: understand the concepts of limit, continuity, differentiability of complex functions.

CO 2: evaluate the integrals of complex functions using Cauchy?s Integral Theorem/Formula and related results.

CO 3: examine various types of transformation of functions of complex variables.

CO 4: demonstrate the expansions of complex functions as Taylor, Power and Laurent Series, Classify singularities and poles.

CO 5: apply the concepts of complex analysis to analyze and address real world problems.

Unit-1
Teaching Hours:15
Analytic Functions
 

Properties of complex numbers, regions in the complex plane, functions of complex variable, limits, limits involving the point at infinity, continuity and differentiability of functions of complex variable. Analytic functions, necessary and sufficient conditions for a function to be analytic.

Unit-2
Teaching Hours:15
Complex Integration and Conformal Mapping
 

Definite integrals of functions, contour integrals and its examples, Cauchy’s integral theorem, Cauchy integral formula, Liouville’s theorem and the fundamental theorem of algebra, elementary transformations, conformal mappings, bilinear transformations.

Unit-3
Teaching Hours:15
Power Series and Singularities
 

Convergence of sequences and series, Taylor series and its examples, Laurent series and its examples, absolute and uniform convergence of power series, zeros and poles.

Text Books And Reference Books:

Dennis G. Zill and Patrick D. Shanahan, A first course in Complex Analysis with Applications, 2nd Ed, Jones & Barlett Publishers, 2011.

Essential Reading / Recommended Reading
  1. J. W. Brown and R. V. Churchill, Complex Variables and Applications, 8th ed., McGraw - Hill International Edition, 2009.
  2. J. Bak and D. J. Newman, Complex analysis, 2nd ed., Undergraduate Texts in Mathematics, Springer-Verlag New York, Inc., New York, 2000.
  3. A. Jeffrey, Complex Analysis and Applications, 2nd ed., CRC Press, Boca Raton 2013.
  4. L. V. Ahlfors, Complex Analysis, 3rd ed., McGraw-Hill Education, 2017.
  5. S. Ponnusamy, Foundations of Complex Analysis, 2nd ed., Narosa Publishing House, Reprint 2021.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment

Project

Problem solving skills

 

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT641A - MECHANICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course description: This course aims at introducing the basic concepts in statistics as well as dynamics of particles and rigid bodies; develop problem solving skills in mechanics through various applications.

Course objectives: This course will help the learner to

COBJ1. Gain familiarity with the concepts of force, triangular and parallelogram laws and conditions of equilibrium of forces.

COBJ2. Analyse and interpret the Lamis Lemma and the resultant of more than one force.

COBJ3. examine dynamical aspect of particles and rigid bodies.

COBJ4. illustrate the concepts of simple harmonic motion and projectiles

 

Learning Outcome

CO1: Compute resultant and direction of forces and examine the equilibrium of a force.

CO2: Apply Lamis's Theorem and Varignon's Theorem in solving problems.

CO3: Analyse the motion of a particle on a smooth surface.

CO4: Discuss the motion of a particles subjected to Simple Harmonic Motion and fundamental concepts Projectiles.

Unit-1
Teaching Hours:15
Forces acting on particle / rigid body
 

Introduction and general principles, force vectors, moments, couple-equilibrium of a particle - coplanar forces acting on a rigid body, problems of equilibrium under forces

Unit-2
Teaching Hours:20
Dynamics of a particle in 2D
 

Velocities and accelerations along radial and transverse directions and along tangential and normal directions; relation between angular and linear vectors, dynamics on smooth and rough plane curves.

Unit-3
Teaching Hours:10
Kinetics of particle and Projectile Motion
 

Simple harmonic motion, Newton’s laws of motion, projectiles. 

Text Books And Reference Books:
  1. A S Ramsey, Statics, CBS Publishers & Distributors, 2004.
  2. A.P. Roberts, Statics and Dynamics with Background in Mathematics, Cambridge University Press, 2003.
Essential Reading / Recommended Reading
  1. S. L. Loney, The elements of statics and dynamics-Part I Statics. 6th ed., Arihant Publications, 2004.
  2. S. L. Loney, The elements of statics and dynamics-Part II Dynamics.6th ed., Arihant Publications, 2004.
  3. P.K.Mittal, Mathematics for degree students, S Chand publications, 2016.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment, Reference work

Mastery of the core concepts

Problem solving skills

10

CIA II

Mid-semester  Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Assignment

Project

Mastery of the core concepts

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT641B - NUMERICAL METHODS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course description: To explore the complex world problems physicists, engineers, financiers and mathematicians require certain methods. These practical problems can rarely be solved analytically. Their solutions can only be approximated through numerical methods. This course deals with the theory and application of numerical approximation techniques.

 

Course objectives: This course will help the learner

COBJ1. To learn about error analysis, solution of nonlinear equations, finite differences, interpolation, numerical integration and differentiation, numerical solution of differential equations, and matrix computation.

COBJ2. It also emphasis the development of numerical algorithms to provide solutions to common problems formulated in science and engineering.

Learning Outcome

CO1: Understand floating point numbers and the role of errors and its analysis in numerical methods.

CO2: Derive numerical methods for various mathematical operations and tasks, such as interpolation, differentiation, integration, the solution of linear and nonlinear equations, and the solution of differential equations.

CO3: Apply numerical methods to obtain approximate solutions to mathematical problems.

CO4: Understand the accuracy, consistency, stability and convergence of numerical methods

Unit-1
Teaching Hours:15
Error analysis, Nonlinear equations, and Solution of a system of linear Equations
 

Errors and their analysis, Floating point representation of numbers, solution of algebraic and Transcendental Equations: Bisection method, fixed point Iteration method, the method of False Position, Newton Raphson method and Mullers method. Solution of linear systems, matrix inversion method, Gauss elimination method, Gauss-Seidel and Gauss-Jacobi iterative methods, modification of the Gauss method to compute the inverse, LU decomposition method.

Unit-2
Teaching Hours:15
Finite Differences, Interpolation, and Numerical differentiation and Integration
 

Finite differences: Forward difference, backward difference and shift operators, separation of symbols, Newton’s formulae for interpolation, Lagrange’s interpolation formulae, numerical differentiation. Numerical integration: Trapezoidal rule, Simpson’s one-third rule and Simpson’s three-eighth rule.

Unit-3
Teaching Hours:15
Numerical Solution of Ordinary Differential Equations
 

Numerical solution of ordinary differential equations, Taylor’s series, Picard’s method, Euler’s method, modified Euler’s method, Runge Kutta methods, second order (with proof) and fourth order (without proof).

Text Books And Reference Books:
  1. C. F. Gerald and P. O. Wheatly, Applied Numerical Analysis, 7th ed., Wesley. 2007.
  2. M. K. Jain, Iyengar, S. R. K. and R. K. Jain, Numerical Methods for Scientific and Engineering Computation, New Age Pvt. Pub, New Delhi, 2012.
  3. R. L. Burden and J. D. Faires, Numerical analysis, Belmont, CA: Thomson Brooks/Cole, 2005.
Essential Reading / Recommended Reading
  1. E. V. Krishnamurthy and S. K. Sen, Applied Numerical Analysis, East West Publication, 1986.
  2. F. Scheid, Schaum's Outline of Numerical Analysis, 2nd ed., Mc.Graw Hill, 2006.
  3. A. Grégoire, Numerical analysis and optimization: an introduction to mathematical modelling and numerical simulation, Oxford: Oxford University Press, 2007.
  4. K. E. Atkinson and W. Han, Elementary numerical analysis. Hoboken, NJ: Wiley, 2004.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Assignment/problem solving

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT641C - DISCRETE MATHEMATICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course description: It is a fundamental course in combinatorics involving set theory, permutations and combinations, generating functions, recurrence relations and lattices.

Course objectives: This course will help the learner to 

COBJ 1: Gain a familiarity with fundamental concepts of combinatorial mathematics.

COBJ 2: Understand the methods and problem solving techniques of discrete mathematics

COBJ 3: Apply knowledge to analyze and solve problems using models of discrete mathematics

Learning Outcome

CO1: Enhance research, inquiry, and analytical thinking abilities.

CO2: Apply the basics of combinatorics in analyzing problems.

CO3: Enhance problem-solving skills.

Unit-1
Teaching Hours:15
Combinatorics
 

Permutations and combinations, laws of set theory, Venn diagrams, relations and functions, Stirling numbers of the second kind, Pigeon hole principle.

Unit-2
Teaching Hours:15
Enumeration
 

Principle of inclusion and exclusion, generating functions, partitions of integers and recurrence relations.

Unit-3
Teaching Hours:15
Lattice Theory
 

Partially ordered set, lattices and their properties, duality principle, lattice homomorphisms, product lattices, modular and distributive lattices, Boolean lattices.

Text Books And Reference Books:
  1. Ralph P. Grimaldi, Discrete and Combinatorial Mathematics – An applied introduction, Pearson Addison Wesley, 5th Edition, 2004.
  2. Rosen, Kenneth. Discrete Mathematics and Its Applications. United Kingdom, McGraw-Hill Education, 2006.
  3. Jongsma Calvin, Discrete Mathematics: Chapter 0, Table of Contents and Preface, Faculty Work: Comprehensive List. Paper 426, 2016.
Essential Reading / Recommended Reading
  1. R. A. Brualdi, Introductory Combinatorics, 5th ed., China Machine Press, 2009.
  2. E. A. Bender and S. G. Williamson, Foundations of combinatorics with applications, Dover Publ., 2007.
  3. J. P. Tremblay and R. Manohar, Discrete Mathematical Structures with Applications to Computer Science, 1st ed., McGraw Hill Education, 2017.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

Test

Written Assignment

Mastery of the core concepts

Problem solving skills

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Written Assignment, Test

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT641D - NUMBER THEORY (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course Description: This course is an introduction to elementary topics of analytical number theory. Topics such as divisibility, congruences and number-theoretic functions are discussed in this course. Some of the applications of these concepts are also included.

Course Objectives: This course will help the learner to

COBJ 1: Engage in sound mathematical thinking and reasoning.

COBJ 2: Analyze, evaluate, or solve problems for given data or information.

COBJ 3: Understand and utilize mathematical functions and empirical principles and processes.

COBJ 4: Develop critical thinking skills, communication skills, and empirical and quantitative skills.

Learning Outcome

CO1: effectively express the concepts and results of number theory.

CO2: understand the logic and methods behind the proofs in number theory.

CO3: solve challenging problems in number theory.

CO4: present specific topics and prove various ideas with mathematical rigour.

Unit-1
Teaching Hours:15
Divisibility
 

The division algorithm, the greatest common divisor, the Euclidean algorithm, the linear Diophantine equation, the fundamental theorem of arithmetic, distribution of primes.

Unit-2
Teaching Hours:15
Linear Congruence
 

Basic properties of congruences, systems of residues, number conversions, linear congruences and Chinese remainder theorem, a system of linear congruences in two variables, Fermat’s Little Theorem and pseudoprimes, Wilson’s Theorem.

Unit-3
Teaching Hours:15
Number Theoretic Functions
 

The Greatest Integer Function, Euler’s Phi-Function, Euler’s theorem, Some Properties of Phi-function. Applications of Number Theory: Hashing functions, pseudorandom Numbers, check bits, cryptography.

 

Text Books And Reference Books:
  1. D. M. Burton, Elementary Number Theory, 7th ed., New Delhi: Tata McGraw-Hill, 2012.
  2. S. Kundu and S. Mazumder, Number Theory and Its Applications, Bocca Raton: CRC Press, 2022.
Essential Reading / Recommended Reading
  1. K. H. Rosen, Elementary Number Theory, 6th ed., New Delhi: Pearson Education India, 2015.
  2. G. Effinger and G. L. Mullen, Elementary Number Theory, Bocca Raton: CRC Press, 2021.
  3. J. Pommersheim, T. K. Marks and E. L. Flapan, Number Theory, New Jersey: John Wiley & Sons, 2009.
  4. J. H. Silverman, A friendly introduction to number theory, London: Pearson Prentice Hall, 2006.
  5. Niven, H.S. Zuckerman and H.L. Montgomery, An introduction to the theory of numbers, 5th ed., New Jersey: John Wiley & Sons, Inc., 2012.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work  

Mastery of the core concepts  

Problem solving skills

13

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

05

CIA III

Written Assignment / Project

Written assignment based on Binary and Decimal representation of integers.

05

Attendance

Attendance

Regularity and Punctuality

   02

ESE

 

Basic, conceptual and analytical knowledge of the subject

25

Total

50

MAT641E - FINANCIAL MATHEMATICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Course Description:Financial Mathematics deals with the solving of financial problems by using Mathematical methods. This course aims at introducing the basic ideas of deterministic mathematics of finance. The course focuses on imparting sound knowledge on elementary notions like simple interest, complex interest (annual and non-annual), annuities (varying and non-varying), loans and bonds.

Course objectives: This course will help the learner to

COBJ 1: gain familiarity in solving problems on Interest rates and Level Annuitiesd

COBJ 2: derive formulae for different types of varying annuities and solve its associated problems

COBJ 3: gain in depth knowledge on Loans and Bonds and hence create schedules for Loan Repayment and Bond Amortization Schedules.

Learning Outcome

CO1: On successful completion of the course, the students should be able to deal with the elementary notions like simple interest, compound interest and Annuities.

CO2: On successful completion of the course, the students should be able to solve simple problems on interest rates, annuities, varying annuities, non-annual interest rates, loans and bonds.

CO3: On successful completion of the course, the students should be able to apply the formulae appropriately in solving problems that mimics real life scenario.

Unit-1
Teaching Hours:15
Interest Rates, Factors and Level Annuities
 

Interest Rates, Rate of discount, Nominal rates of interest and discount, Constant force of interest, Force of interest, Inflation, Equations of Value and Yield Rates, Annuity-Immediate, Annuity-Due, Perpetuities, Deferred Annuities and values on any date, Outstanding Loan Balances (OLB)

Unit-2
Teaching Hours:15
Varying Annuities
 

Non-level Annuities, Annuities with payments in Geometric Progression, Annuities with payment in Arithmetic Progression, Annuity symbols for non-integral terms, Annuities with payments less/more frequent than each interest period and payments in Arithmetic Progression, Continuously Payable Annuities.

Unit-3
Teaching Hours:15
Loans Repayment and Bonds
 

Amortized loans and Amortization Schedules, The sinking fund method, Loans with other repayment patterns, Yield rate examples and other repayment patterns, Bond symbols and basic price formula, Other pricing formula for bonds, Bond Amortization Schedules, Valuing a bond after its date of issue.

Text Books And Reference Books:

 L. J. F. Vaaler and J. W. Daniel, Mathematical interest theory. Mathematical Association of America, 2009.

Essential Reading / Recommended Reading
  1. S. J. Garrett and J. J. McCutcheon, An introduction to the mathematics of finance: a deterministic approach, 2nd ed., Amsterdam: Elsevier/Butterworth-Heinemann, 2013.
  2. A. Černý, Mathematical techniques in finance: tools for incomplete markets. 2nd ed., NJ: Princeton University Press, 2009.
  3. C. Ruckman and J. Francis, Financial mathematics: a practical guide for actuaries and other business professionals. 2nd ed., Weatogue, CT: BPP Professional Education, 2005.
Evaluation Pattern

 

Component

Mode of Assessment

Parameters

Points

CIA I

MCQ

Written Assignment

Reference work

Mastery of the core concepts  

Problem solving skills

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

25

CIA III

Assignment

Problem solving skills

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MAT651 - COMPLEX ANALYSIS USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course Description: This course will enable students to have hands on experience in constructing analytic functions, verifying harmonic functions, illustrating Cauchy’s integral theorem and bilinear transformations and in illustrating different types of sequences and series using Python.

Course Objectives: This course will help the learner to

COBJ 1:Python language using jupyter interface

COBJ 2:Solving basic arithmetic problems using cmath built-in commands

COBJ 3:Solving problems using cmath.

Learning Outcome

CO 1: acquire proficiency in using Python and cmath functions for processing complex numbers.

CO 2: skilful in using Python modules to implement Milne-Thompson method.

CO 3: expertise in illustrating harmonic functions and demonstrating Cauchy?s integral theorem Representation of conformal mappings using Matplotlib.

Unit-1
Teaching Hours:30
Proposed Topics:
 
  1. Cmath functions for complex numbers
  2. Graphical Illustration of the limit of a complex sequence
  3. Verifying C-R equations
  4. Harmonic functions and harmonic conjugates
  5. Implementation of Milne-Thomson method of constructing analytic functions
  6. Examples connected with Cauchy’s integral theorem
  7. llustration of conformal mapping
  8. Linear and bilinear transformations
  9. Convergence/divergence of complex series
  10. Applications of complex analysis in various fields
Text Books And Reference Books:

H P Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer, 2016.

Essential Reading / Recommended Reading
  1. B E Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. C Hill, Learning Scientific Programming with Python, Cambridge Univesity Press, 2016.
  3. A. Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, no starch press:San Fransisco, 2015.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT651A - MECHANICS USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course Description: This course aims at enabling the students to explore and study the statics and dynamics of particles in a detailed manner using Python. This course is designed with a learner-centric approach wherein the students will acquire mastery in understanding mechanics using Python.

Course objectives: This course will help the learner to

COBJ 1: Acquire skill in usage of suitable functions/packages of Python.

COBJ 2: Gain proficiency in using Python to solve problems on Mechanics.   

Learning Outcome

CO1: Acquire proficiency in using different functions of Python to study Differential Calculus. Mechanics.

CO2: Demonstrate the use of Python to understand and interpret the dynamical aspects of Python.

CO3: Use Python to evaluate the resultant of forces and check for equilibrium state of the forces.

CO4: Be familiar with the built-in functions to find moment and couple.

Unit-1
Teaching Hours:30
Proposed Topics
 
  1. Introduction to Python: Some useful shortcuts; variables; input/output; relational operators, logical operators; conditional statements; lists and matrices\
  2. Resultant of a number of forces: Resultant of two forces in the same plane, resultant of any number of forces, resultant of any number of forces
  3. Components of a given force: Components of a force in horizontal and vertical directions, components of a force in any two given directions
  4. Resultant force of parallel forces: Resultant force of two parallel like forces, resultant force of two parallel alike forces
  5. Moments and torques: Moment from magnitude and perpendicular distance, equilibrium of two moments
  6. Projectiles
  7. Simple harmonic motion
Text Books And Reference Books:
  1. B. E. Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. Anders Malthe-Sørenssen, Elementary Mechanics Using Python: A Modern Course Combining Analytical and Numerical Techniques (Undergraduate Lecture Notes in Physics) 2015.
  3. C. Hill, Learning Scientific Programming with Python, Cambridge University Press, 2016.
Essential Reading / Recommended Reading

A. Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, no starch press: San Fransisco, 2015.

Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT651B - NUMERICAL METHODS USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course Description: This course will help the students to have an in depth knowledge of various numerical methods required in scientific and technological applications. Students will gain hands on experience in using Python for illustrating various numerical techniques.

Course Objectives: This course will help the learner to

COBJ 1: Develop the basic understanding of numerical algorithms and skills to implement algorithms to solve mathematical problems using Python.

COBJ 2: To develop the basic understanding of the applicability and limitations of the techniques.

Learning Outcome

CO1: Implement a numerical solution method in a well-designed, well-documented Python program code.

CO2: Interpret the numerical solutions that were obtained in regard to their accuracy and suitability for applications

CO3: Present and interpret numerical results in an informative way.

Unit-1
Teaching Hours:30
Proposed topics
 
  1. Some basic operations in Python for scientific computing                          
  2. Solution of Algebraic and Transcendental Equations  
    • Bisection method
    • Fixed point Iteration method
    • The method of False Position
    • Newton-Raphson method
  3. Solution of linear systems
    • Gauss Elimination method
    • Gauss-Seidel Iterative method
    • Gauss-Jacobi Iterative method
    • LU Decomposition method
  4. Numerical Differentiation and Integration
  5. Solution of Differential Equations
    • Euler’s method
    • Runge Kutta method
Text Books And Reference Books:

J. Kiusalaas, Numerical methods in engineering with Python 3, Cambridge University press, 2013.

Essential Reading / Recommended Reading

H. Fangohr, Introduction to Python for Computational Science and Engineering (A beginner’s guide), University of Southampton, 2015.

Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT651C - DISCRETE MATHEMATICS USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course description: This course aims at providing hands on experience in using Python functions to illustrate the notions of combinatorics, set theory and relations.

Course objectives: This course will help the learner to

COBJ1. Gain a familiarity with programs on fundamental concepts of Combinatorial Mathematics

COBJ2. Understand and apply knowledge to solve combinatorial problems using Python

Learning Outcome

CO1: Attain sufficient skills in using Python functions

CO2: Demonstrate programming skills in solving problems related to applications of computational mathematics.

Unit-1
Teaching Hours:30
Proposed Topics
 
  1. Permutations
  2. Combinations
  3. Set construction and set operations
  4. Using Venn diagrams to visualize relationships between sets
  5. Recurrence relations
  6. Partially ordered sets
Text Books And Reference Books:
  1. Amit Saha, Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!, no starch press:San Fransisco, 2015.
  2. H P Langtangen, A Primer on Scientific Programming with Python, 2nd ed., Springer, 2016.
Essential Reading / Recommended Reading
  1. B E Shapiro, Scientific Computation: Python Hacking for Math Junkies, Sherwood Forest Books, 2015.
  2. C Hill, Learning Scientific Programming with Python, Cambridge University Press, 2016.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT651D - NUMBER THEORY USING PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course Description: This course will help the students to gain hands-on experience in using Python for illustrating various number theory concepts such as the divisibility, distribution of primes, number conversions, congruences and applications of number theory.

Course Objectives: This course will help the learner to

COBJ 1: Be familiar with the built- in functions required to deal with number theoretic concepts and operations.

COBJ 2: Develop programming skills to solve various number theoretic concepts.

COBJ 3: Gain proficiency in symbolic computation using python.

Learning Outcome

CO1: to solve problems in number theory, number conversions.

CO2: to demonstrate the understanding of number theory concepts.

CO3: to model and solve practical problems using number theoretic concepts.

Unit-1
Teaching Hours:30
Proposed Topics:
 
  1. Introduction to packages and libraries in Python.
  2. Division algorithm.
  3. Hexadecimal, octal and binary representation of the integers.
  4. Euclid algorithm.
  5. Prime factorisation of integers.
  6. Solution of a system of linear congruences.
  7. Number theoretic functions τ, σ and φ.
  8. Hashing functions, pseudorandom numbers.
  9. Parity check bits
  10. Cryptography
Text Books And Reference Books:

J.C. Bautista, Mathematics with Python Programming, Lulu.com, 2014.

Essential Reading / Recommended Reading

M. Litvin and G. Litvin, Mathematics for the Digital Age and Programming in Python, Skylight Publishing, 2010.

Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT651E - FINANCIAL MATHEMATICS USING EXCEL AND PYTHON (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course Description: The course aims at providing hands on experience in using Excel/Python programming to illustrate the computation of constant/varying force of interest, continuously payable varying/non-varying annuities, increasing/decreasing annuity immediate/due, loans and bonds.

Course objectives: This course will help the learner to

COBJ 1: acquire skill in solving problems on Financial Mathematics using Python.

COBJ 2: gain proficiency in using the Python programming skills to solve problems on Financial Mathematics.

Learning Outcome

CO1: demonstrate sufficient skills in using Python programming language for solving problems on Financial Mathematics.

CO2: apply the notions on various types of interests, annuities, loans and bonds, by solving problems using Python.

Unit-1
Teaching Hours:30
Proposed Topics
 
  1. Force of interest
  2. Level Annuities
  3. Outstanding Loan balances
  4. Annuities with payments in Geometric Progression
  5. Annuities with payments in Arithmetic Progression
  6. Continuously Payable annuities
  7. Amortization Loans and Amortization Schedules
  8. Bond Amortization Schedules
Text Books And Reference Books:
  1. Y. Yan, Python for finance: financial modeling and quantitative analysis explained.  2nd ed., Packt Publishing, 2017. 
  2. A. L. Day, Mastering Financial Mathematics in Microsoft Excel - A practical guide for business calculations, 3rd ed., Pearson Education Limited, 2015.
Essential Reading / Recommended Reading
  1. L. J. F. Vaaler and J. W. Daniel, Mathematical interest theory. 2nd ed., Mathematical Association of America, 2009.
  2. J. M. Weiming, Mastering python for finance understand, design, and implement state of-the-art mathematical and statistical applications used in finance with Python. Packt Publishing, 2015. 
  3. M. Humber, Personal finance with Python: using pandas, requests, and recurrent.  1st ed., Apress, 2018. 
  4. S. Fletcher and C. Gardner, Financial modeling in Python. Wiley, 2009.
  5. S. D. Promislow, Fundamentals of Acturaial Mathematics, 3rd ed., John Wiley and Sons Limited, 2015.
Evaluation Pattern

The course is evaluated based on continuous internal assessments (CIA) and the lab e-record. The parameters for evaluation under each component and the mode of assessment are given below.

Component

Parameter

Mode of  Assessment

Maximum

Points

CIA I

Mastery of the  concepts

Lab Assignments

20

CIA II

Conceptual clarity and analytical skills

Lab Exam - I

10

Lab Record

Systematic documentation of the lab sessions.

e-Record work

07

Attendance

Regularity and Punctuality

Lab attendance

03

95-100% : 3

90-94%   : 2

85-89%   : 1

CIA III

Proficiency in executing the commands appropriately,.

Lab Exam - II

10

Total

50

MAT681 - PROJECT ON MATHEMATICAL MODELS (2021 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Course description: The course aims at providing hands on experience in analyzing practical problems by formulating the corresponding mathematical models.

Course objectives: This course will help the learner to

 COBJ1. Develop positive attitude, knowledge and competence for research in Mathematics

Learning Outcome

CO1: Demonstrate analytical skills.

CO2: Apply computational skills in Mathematics

Unit-1
Teaching Hours:75
PROJECT
 

Students are given a choice of topics in Mathematical modelling at the undergraduate level with the approval of HOD. Each candidate will work under the supervision of the faculty.  Project Coordinator will allot the supervisor for each candidate in consultation with the HOD at the end of the fifth  semester.

Project need not be based on original research work. Project could be based on the review of research papers that are at the undergraduate level.

Each candidate has to submit a dissertation on the project topic followed by viva voce examination. The viva voce will be conducted by the committee constituted by the head of the department which will have an external and an internal examiner. The student must secure 50% of the marks to pass the examination.  The candidates who fail must redo the project as per the university regulations.

Proposed Topics for Project: 

  1. Mathematical Modeling using Graphs/Networks
  2. Mathematical Modeling using Optimization Techniques
  3. Mathematical Modeling using Linear Algebra
  4. Mathematical Modeling using Differential Equations
  5. Mathematical Modeling using Calculus of Several Variables. (Proficiency in solving PDE may be required)
  6. Developing a new Mathematics library for FOSS tools
Text Books And Reference Books:

As per the field of reserach.

Essential Reading / Recommended Reading

As per the field of reserach.

Evaluation Pattern

 

Component Maximum Marks
Proposal Presentation 10
Progress Report / Presentation-I 20
Progress Report / Presentation-II 20
Final Viva Voce examination 50
Final Project Report 40
Research Publication 10
Total 150

STA631 - TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data.

Learning Outcome

CO1: Demonstrate the approach and analyze univariate time series

CO2: Infer the difference between various time series models like AR, MA, ARMA and ARIMA models

CO3: Apply the various forecasting techniques to predict the future observations for real time data.

Unit-1
Teaching Hours:15
Introduction to Time Series and Stochastic Process
 

Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing.

Unit-2
Teaching Hours:10
Stationary Time Series Models
 

Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function.

Unit-3
Teaching Hours:10
Estimation of ARMA Models
 

Estimation of ARMAmodels: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, Residual analysis and diagnostic checking.

Unit-4
Teaching Hours:10
Nonstationary Time Series Models
 

Concept of non-stationarity, general unit root tests for testing non-stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models

Text Books And Reference Books:

1. George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey, 2016.

2. Montgomery D.C, Jennigs C. L and Kulachi M, Introduction to Time Series analysis and Forecasting, 2 nd Edition,John Wiley & Sons, Inc., New Jersey, 2016.

Essential Reading / Recommended Reading

1. Anderson T.W., The Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey, 2011.

2. Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer, 2011.

3. Brockwell P.J and Davis R.A, Times series: Theory and Methods, 2nd Edition, Springer-Verlag, 2009.

4. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition (Reprint), Sultan Chand and Sons, 2018.

Evaluation Pattern

CIA 50% ESE 50%

STA641A - APPLIED STATISTICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

This course is designed to teach demographic methods, mortality and fertility rates, concept of index numbers and their usages are explained. Demand analysis helps students to understand the various statistical tools used in demand and supply sector. Educational and psychological statistics are used to emphasize the usage of statistics in real life.

Learning Outcome

CO1: Demonstrate the demographic profiles, mortality and fertility rates.

CO2: Infer the concepts of Demand and supply and their importance

CO3: Demonstrate the importance of index numbers and their usage.Demonstrate the importance of index numbers and their usage.

Unit-1
Teaching Hours:15
Demographic Methods
 

Sources of demographic data-census – register - ad-hoc surveys - hospital records - demographic profiles of Indian census - questionnaire - errors in these data and their adjustment - Measurements of Mortality-CDR, SDR (w.r.t. age and sex), IMR - standardized death rate - complete life table -its main features and uses - Measurements of fertility and reproduction-CBR- General, Age-specific and total fertility rates - GRR and NRR.

Unit-2
Teaching Hours:10
Index Numbers
 

 

Introduction - different types of index numbers - criteria for index numbers - construction of index numbers of prices and quantities - cost of living index numbers - uses and limitations of index numbers.

Unit-3
Teaching Hours:10
Demand Analysis
 

 

Demand and Supply - Price elasticity of demand - Partial and Cross elasticities of demand - Types of data required for estimating elasticity - Pareto’s Law of income distribution - Unity function.

Unit-4
Teaching Hours:10
Psychological and Educational statistics
 

Scaling of Mental tests and Psychological data - Scaling of scores on a test - Z-score and scaling

 

-standardized scores - normalized scores - computation of T-scores for a given frequency distribution - comparison of T- scores and standardized scores - percentile scores - scaling of rankings and ratings in terms of normal curves - Intelligent tests - intelligent quotient and educational quotient.

Text Books And Reference Books:

 

  1. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition (Reprint), Sultan Chand and Sons, New Delhi, 2018.

  2. Ken Black, Applied Business Statistics: Making Better Business Decisions, 7th Edition, Wiley International, US, 2012.

Essential Reading / Recommended Reading
  1. Mukhopadhyay P, Mathematical Statistics, 2nd edition revised reprint, Books and Allied

(P) Ltd, 2016.

 

  1. Borowiak D.S and Shapiro A.F, Financial and Actuarial Statistics: An Introduction, 2nd Edition, CRC Press, Boca Raton, 2013.

  2. Goon A.M, Gupta M.K and Dasgupta B, An Outline of Statistical Theory (Vol.1), 4th Edition, World Press, Kolkata, 2016.

Evaluation Pattern

CIA 50%

ESE 50%

STA641B - STATISTICAL QUALITY CONTROL (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is designed to introduce the application of statistical tools on industrial environment to study, analyze and control the quality of products.

Learning Outcome

CO1: Demonstrate the concepts control charts and sampling plans to improve the quality standards of the products.

CO2: Apply the idea of Reliability theory to control the quality of industrial outputs.

Unit-1
Teaching Hours:15
Introduction to SQC
 

 

Quality: Definition - dimensions of quality - historical perspective of quality control - historical perspective of Quality Gurus - Quality Hall of Fame - Quality system and standards: Introduction to ISO quality standards - Quality registration - Statistical Process Control - Seven tools of SPC, chance and assignable Causes - Statistical Control Charts - Construction and Statistical basis of 3-σ Control charts - Rational Sub-grouping.

Unit-2
Teaching Hours:10
Statistical Process Control
 

 

Control charts for variables: X-bar & R-chart, X-bar & s-chart - Control charts for attributes: np- chart, p-chart, c-chart and u-chart - Comparison between control charts for variables - control charts for attributes - Analysis of patterns on control chart - estimation of process capability.

Unit-3
Teaching Hours:10
Statistical Product Control
 

 

Acceptance sampling plan: Principle of acceptance sampling plans - Single and Double sampling plan - OC, AQL, LTPD, AOQ, AOQL, ASN, ATI functions with graphical interpretation - use and interpretation of Dodge and Romig’s sampling inspection plan tables.

Unit-4
Teaching Hours:10
Reliability
 

 

Reliability concepts - Reliability of components and systems - Life distributions - reliability functions - hazard rate - common life distributions-Exponential, Gamma and Weibull - System reliability - Series, parallel, stand by systems, r/n systems - Complex systems - Fault tree and event tree analysis - link between quality and reliability.

Text Books And Reference Books:

 

  1. Montgomery D.C, Introduction to Statistical Quality Control, 8th edition, Wiley India (P) Ltd, 2019.

  2. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th edition (Reprint), Sultan Chand and Sons, India, 2019.

Essential Reading / Recommended Reading

 

  1. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, 7th edition, Wiley Publication, 2018.

  2. Renyan J, Introduction to Quality and Reliability Engineering, 1st Edition, Springer, 2015.

  3. Schilling E.G and Neubaer D.V, Acceptance sampling in Quality Control, 3rd edition, CRC Press, Boca Raton, 2017.

Evaluation Pattern

CIA 50%

ESE 50%

STA641C - BIOSTATISTICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

This course is designed as an application of statistics in medical sciences. The concepts of bioassays, quantitative epidemiology and survival analysis are introduced. R programming is used to analyze the biomedical data.

Learning Outcome

CO1: Demonstrate the basic biological concepts in Statistical genetics

CO2: Infer the bioassays, dose-response estimation, and dose-allocation schemes

CO3: Demonstrate the concepts in epidemiology and design and analysis of epidemiological studies.

Unit-1
Teaching Hours:15
Introduction to Statistical Genetics
 

Basic biological concepts in genetics - Mendel’s law - Hardy Weinberg equilibrium - estimation of allele frequency - approach to equilibrium for X-linked gene - The law of natural selection - mutation - genetic drift.

Unit-2
Teaching Hours:10
Bioassays
 

The purpose and structure of biological assay - types of biological assays - direct assays - ration estimates - asymptotic distributions: Feller’s theorem - Regression approach to estimating dose response – relationships - Logit and Probit approaches when dose-response curve for standard preparation is unknown - quantal responses - methods of estimation of parameters - estimation of extreme quantiles - dose allocation schemes.

Unit-3
Teaching Hours:10
Quantitative Epidemiology
 

Introduction to modern epidemiology - principles of epidemiological investigation - surveillance and disease monitoring in populations - Epidemiologic measures: Organizing and presenting epidemiologic data - measure of disease frequency - measures of effect and association - causation and causal inference - Design and analysis of epidemiologic studies - Types of studies - case-control studies - cohort studies - cross over design - regression models for the estimation of relative risk.

Unit-4
Teaching Hours:10
Survival Analysis
 

 

Introduction to survival analysis - examples and its characteristics - types of survival analysis - survival functions and hazard function - life distributions: Exponential, Gamma, Weibull, Lognormal and Pareto - Linear failure rate - Life tables - KM survival curves and log-rank test - comparison of survival curves - Cox-PH model and its characteristics - stratified Cox-regression model - Cox-regression with time dependent covariates.

Text Books And Reference Books:

 

  1. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, 2014.

  2. Lange K, Mathematical and Statistical Methods for Genetic Analysis, Springer, 2008.

Essential Reading / Recommended Reading

 

  1. Danial W.W, Cross C.L, Biostatistics: Basic concepts and Methodology for the Health Sciences, 10th Edition, John Wiley, 2014.

  2. Indranil S, Bobby P, Essential of Biostatistics, 2nd Edition, Academic Publishers, 2016.

Evaluation Pattern

CIA 50%

ESE 50%

STA641D - STATISTICAL GENETICS (2021 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course is designed to introduce the basic concepts of genetics,estimation of linkage, Application and extension of the equilibrium law under different situation.This course also introduces the concept of inbreeding, Heritability, Repeatability and Geneticcorrelationin large populations.

Learning Outcome

CO1: Demonstrate the basic concepts of genetics and their applications.

CO2: Demonstrate Fisher's fundamental theorem of natural selection with different forces.

CO3: Demonstrate methods of estimation of Heritability, Repeatability and Genetic correlation.

Unit-1
Teaching Hours:15
Segregation and Linkage
 

Physical basis of inheritance - Analysis of segregation - detection and estimation of linkage forqualitative characters - Amount of information about linkage - combined estimation - disturbedsegregation.

Unit-2
Teaching Hours:10
Equilibrium law and sex-linked genes
 

Gene and genotypic frequencies - Random mating and Hardy - Weinberg law - Application andextensionoftheequilibriumlaw-Fisher'sfundamentaltheoremofnaturalselection-Disequilibriumduetolinkagefortwopairsofgenes-sex-linkedgenes-Theory ofpathcoefficients.

Unit-3
Teaching Hours:10
Inbreeding and Systematic forces
 

Conceptsof inbreeding- regular systemof inbreeding- Forcesaffecting gene frequency -selection, mutation and migration - equilibrium between forces in large populations - Randomgeneticdrift-Effect of finitepopulation size.

Unit-4
Teaching Hours:10
Association and selection index
 

Correlations between relatives – Heritability - Repeatability and Genetic correlation - Responsedue to selection - Selection index and its applications in plants and animals - improvementprogrammes-Correlatedresponse to selection.

Text Books And Reference Books:

1. Laird N.M and Christoph L, The Fundamental of Modern Statistical Genetics, Springer,2011.

 

2. Balding DJ, Bishop M & Cannings C, Hand Book of Statistical Genetics, 3rd edition, JohnWiley,2007.

Essential Reading / Recommended Reading

1.     Benjanmin M.N, Manuel A.R.F, Sarah E.M, Danielle P, Statistical Genetics, CRC Press,2008.

 

2.     ShizhongXu,Principles ofStatisticalGenomics, Springer,2013.

Evaluation Pattern

CIA 50%

ESE 50%

STA651 - TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

The course is designed to provide a practical exposure to the students in Time Series analysis

Learning Outcome

CO1: Demonstrate the analyses of univariate time series for real time data

CO2: Forecast the future values of a given univariate time series.

Unit-1
Teaching Hours:30
Practical assignments using R programming:
 

1. Time series plots, Decomposition of time series.

2. ACF, PACF plots and their interpretation

3. Smoothing techniques – Simple, Moving average methods, Differenced series.

4. Fitting Autoregressive

5. Fitting of Moving average models.

6. Model identification using ACF and PACF.

7. Residual analysis and diagnostic checking of AR models

8. Residual analysis and diagnostic checking of MA models

9. Testing for stationarity.

10. Fitting ARMA, ARIMA models.

11. Residual analysis and diagnostic checking of ARMA , ARIMA models

12. Forecasting using ARIMA models.

Text Books And Reference Books:

 

1. George E. P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time Series analysis Forecasting and Control, 5th Edition, John Wiley & Sons, Inc., New Jersey, 2016.

2. Montgomery D.C, Jennigs C. L and Kulachi M,Introduction to Time Series analysis and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey, 2016.

Essential Reading / Recommended Reading

 

1. Anderson T.W,Statistical Analysis of Time Series, John Wiley& Sons, Inc., New Jersey, 1971.

2. Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer, 2011.

3. Brockwell P.J and Davis R.A, Times series: Theory and Methods, 2nd Edition, Springer-Verlag, 2009.

4. Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition (Reprint), Sultan Chand and Sons, 2018.

Evaluation Pattern

CIA 50%

 

ESE 50%

STA652A - APPLIED STATISTICS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is designed to teach practical problems in demographic methods,Demand analysis, indexnumbers and educational statistics.

Learning Outcome

CO1: Demonstrate and evaluate demographic profiles, calculate various index numbers.

CO2: Apply concepts of Psychological and educational statistics for real life problems.

Unit-1
Teaching Hours:30
Practical assignments using EXCEL:
 

 1.     Measures of Mortality and IMR

2.     Measures of fertility

 3.     Construction of life tables.

 4.     Construction of weighted and unweighted Index numbers

 5.     Construction of Price and Quantity index numbers

 6.     Tests for index numbers

 7.     Construction of Cost of living index numbers

 8.     Computation of T-scores for a given frequency distribution

 

9.     Psychological and educational statistics-1 (Computation of various scores)

 

10.  Psychological and educational statistics-2 (Scaling of ranking & ratings)

Text Books And Reference Books:

1.     Gupta S.C and Kapoor V.K, Fundamentals of Applied Statistics, 4th Edition (Reprint),SultanChand and Sons, New Delhi, 2018.

2.Ken Black, Applied Business Statistics: Making Better Business Decisions, 7th Edition,WileyInternational, US, 2012.

Essential Reading / Recommended Reading

1.     MukhopadhyayP,MathematicalStatistics,2ndeditionrevisedreprint,BooksandAllied

(P)Ltd,2016.

2.BorowiakD.SandShapiroA.F,FinancialandActuarialStatistics:AnIntroduction,2ndEdition,CRCPress, BocaRaton, 2013.

3.  3.   GoonA.M,GuptaM.KandDasguptaB,AnOutlineofStatisticalTheory(Vol.1),4thEdition,World Press, Kolkata, 2016.

Evaluation Pattern

CIA 50%

ESE 50%

STA652B - STATISTICAL QUALITY CONTROL PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

The course is designed to provide a practical exposure to the students for the various statistical quality control tools.

Learning Outcome

CO1: Demonstrate the variable and attribute control charts for industrial data

CO2: Demonstrate the sampling plans using R programming/EXCEL.

Unit-1
Teaching Hours:30
Practical assignments using R programming/EXCEL
 
  1. X bar and R charts (Standard values known and unknown)

 

 

  1. X bar charts (Standard values known and unknown)

  2. np and p charts (Standard values known and unknown)

  3. c and u charts (standard values known and unknown)

  4. Pareto charts

  5. Fish Bone diagram using EXCEL

  6. Construction of OC, AOQ, ASN and ATI curves for single sampling plan under the conditions of binomial distribution.

  7. Construction of OC, AOQ, ASN and ATI curves for single sampling plan under the conditions of binomial distribution.

  8. Calculating sample size and acceptance number for single sampling plan using unity value approach.

  9. Construction of OC, AOQ, ASN and ATI curves for double sampling plan under the conditions of binomial distribution.

  10. Reliability and hazard functions

  11. Fault tree analysis using EXCEL and R

Text Books And Reference Books:

 

  1. Montgomery D.C, Introduction to Statistical Quality Control, 8th edition, Wiley India (P) Ltd, 2019.

Essential Reading / Recommended Reading

 

  1. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, 7th edition, Wiley Publication, 2018.

Evaluation Pattern

CIA 50%

ESE 50%

STA652C - BIOSTATISTICS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

 

 This course is designed to teach practical bio statistical problems using statistical softwares.

Learning Outcome

CO1: Demonstrate and evaluate bio statistical models using R programming.

Unit-1
Teaching Hours:30
Practical assignments using R programming:
 

 

  1. Regression approach of estimating the dose response.
  2. Logit and Probit approaches for dose response

  3. Estimation of Logit and Probit models

  4. Calculation of Survival and Hazard functions using Exponential distribution

  5. Calculation of Survival and Hazard functions using gamma distribution

  6. Calculation of Survival and Hazard functions using Weibull distribution

  7. Parato charts and Life tables

  8. Kaplan-Meier curves

  9. Fitting of Cox-regression models

  10. Fitting of Cox regression with time dependent covariates

Text Books And Reference Books:

 

  1. Lange K, Mathematical and Statistical Methods for Genetic Analysis, Springer, 2008.

Essential Reading / Recommended Reading

Danial W.W, Cross C.L, Biostatistics: Basic concepts and Methodology for the Health Sciences, 10th Edition, John Wiley, 2014.

Evaluation Pattern

CIA 50%

ESE 50%

STA652D - STATISTICAL GENETICS PRACTICAL (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is designed to teach practical biostatistical problems using statistical softwares.

Learning Outcome

CO1: Demonstrate and evaluate bio statistical models using R programming.

Unit-1
Teaching Hours:30
Practical assignments using R programming:
 

1.     Analysis of segregation,detection and estimation of linkage

2.     Estimation of Amount of information about linkage

3.     Calculation of combined estimation of linkage

4.     Estimation of disequilibrium due to Linkage for two pairs of genes

5.     Estimation of path coefficients

6.     Estimation of equilibrium between forces in large populations

7.     Correlations between relatives and Heritability

8.     Correlations between Repeatability and Genetic correlation

Text Books And Reference Books:

1.     Laird N.M and Christoph L, The Fundamental of Modern Statistical Genetics, Springer,2011.           

2.   Balding DJ, Bishop M & Cannings C, Hand Book of Statistical Genetics, 3rd edition, JohnWiley,2007.

Essential Reading / Recommended Reading

1.     Benjanmin M.N, Manuel A.R.F, Sarah E.M, Danielle P, Statistical Genetics, CRC Press,2008.

2.     ShizhongXu,Principles ofStatisticalGenomics, Springer,2013.

Evaluation Pattern

CIA 50%

ESE 50%