Syllabus for
MTech (Data Science)
Academic Year  (2024)

Assessments(Theory & Practical)

•  Continuous Internal Assessment (CIA) for Theory = 100 Marks
•  Continuous Internal Assessment (CIA) for Practical  = 50 Marks

Department of Computer Science & Engineering started of journey to produce qualified Engineers to society with variety of skills. The department offers the degrees Bachelor of Technology, Master of Technology, and Doctor of Philosophy in the areas of Computer Science Engineering and Information Technology. The department has rich knowledge pool of faculty resource who are well trained in various fields like Artificial Intelligence, Machine learning, Computer Vision, Algorithms design, Cryptography, Computer Networking, Data mining, Data science, BIG DATA, Digital Image Processing, text mining, knowledge representation, soft computing, Cloud computing, etc.. The department has wide variety of labs setup namely open source lab , Machine learning lab , CISCO Networking Lab etc.. specifically for students for their lab curriculum and for their research.

VISION: To fortify Ethical Computational Excellence

MISSION: Imparts core and state-of-the-art knowledge in the areas of Computation and Information Technology. Promotes the culture of research and inspires innovation. Acquaints the students with the latest industrial practices, team building and entrepreneurship. Sensitizes the students to the environmental, social & ethical needs of society.

M. Tech in Data Science is a two year, four semester post-graduate programme with an objective to impart the knowledge on methodologies, techniques and concepts related to data science which includes mathematics, statistics, data warehousing, data mining, machine learning and visualization techniques. The main objective of this program is to provide one of the best post graduate education to students so that they can meet the growing regional, national and international need for highly qualified personnel in the fields of data science, natural language processing and artificial intelligence. The curriculum is framed by experienced academic and industrial expertise, by considering current as well as future demands of enterprises. 

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: PO2: Analyze complex engineering problems critically, apply independent judgment for synthesizing information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context.

PO3: PO3: Think laterally and originally, conceptualize and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise.

PO4: PO4: Apply basic and advanced Data Science knowledge that prepares for efficiency, leadership roles in a variety of career paths and integrates ethics.

PO5: PO5: Develop domain knowledge in mathematical, statistical, Data Science and AI techniques to create modelling, analysis and processing of large multidimensional data sets.

This course is aimed at providing a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments.

Security Fundamentals-4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security. Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber-Security Initiative CNCI – Legalities.

Security Fundamentals-4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security. Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber-Security Initiative CNCI – Legalities.

Cyber Attack and Cyber Services Computer Virus – Computer Worms – Trojan horse. Vulnerabilities -  Phishing -  Online Attacks – Pharming - Phoarging  –  Cyber Attacks  -  Cyber Threats -  Zombie- stuxnet - Denial of Service Vulnerabilities  - Server Hardening-TCP/IP attack-SYN Flood.

Cyber Attack and Cyber Services Computer Virus – Computer Worms – Trojan horse. Vulnerabilities -  Phishing -  Online Attacks – Pharming - Phoarging  –  Cyber Attacks  -  Cyber Threats -  Zombie- stuxnet - Denial of Service Vulnerabilities  - Server Hardening-TCP/IP attack-SYN Flood.

Cyber Security Management Risk Management and Assessment - Risk Management Process - Threat Determination Process -Risk Assessment - Risk Management Lifecycle. Security Policy Management - Security Policies - Coverage Matrix. Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning Process.

Cyber Security Management Risk Management and Assessment - Risk Management Process - Threat Determination Process -Risk Assessment - Risk Management Lifecycle. Security Policy Management - Security Policies - Coverage Matrix. Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning Process.

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Black box- white box Architectural Integration:  Security Zones - Devices viz Routers, Firewalls, DMZ. Configuration Management - Certification and Accreditation for Cyber-Security.

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Black box- white box Architectural Integration:  Security Zones - Devices viz Routers, Firewalls, DMZ. Configuration Management - Certification and Accreditation for Cyber-Security.

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services. Securing Communications:  Securing Services - Transport – Wireless - Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems:   Intrusion - Defense in Depth - IDS/IPS -IDS/IPS Weakness and Forensic Analysis. Cyber Evolution: Cyber Organization - Cyber Future

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services. Securing Communications:  Securing Services - Transport – Wireless - Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems:   Intrusion - Defense in Depth - IDS/IPS -IDS/IPS Weakness and Forensic Analysis. Cyber Evolution: Cyber Organization - Cyber Future

R2. AtulKahate, “Cryptography and Network Security”, 2nd Edition, Tata McGrawHill.

R3. Nina Godbole, SunitBelapure, “Cyber Security”, Wiley India 1st Edition 2011.

R4. .Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, “Cyber Security Policy Guidebook”, Wiley; 1 edition , 2012

R5. Dan Shoemaker and Wm. Arthur Conklin, “Cybersecurity: The Essential Body Of Knowledge”,   Delmar Cengage Learning; 1 edition (May 17, 2011) .

R6.William Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3^rdEdition2002.

Data-driven decision making is becoming more common in organizations and businesses. In fact, database systems are at the center of the information systems strategies of most organizations. Users at any level of an organization can expect to work with and use database systems often. So, the ability to use these systems, which includes knowing what they can do and what they can't do, figuring out whether to access data directly or through technical experts, and knowing how to find and use the information well, became essential in every industry. Also, being able to design new systems and applications for them is a clear advantage and a necessity in the modern world. One type of database system that is widely used and the main focus of this course is the Relational Database Management System (RDBMS).

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables.

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables.

Introduction to Normalization using Functional and Multivalued Dependencies: Informal design guidelines for relation schema, Functional Dependencies, Normal Forms based on Primary Keys, Second and Third Normal Forms

Introduction to Normalization using Functional and Multivalued Dependencies: Informal design guidelines for relation schema, Functional Dependencies, Normal Forms based on Primary Keys, Second and Third Normal Forms

Simple queries in SQL, queries involving more than one relation, sub queries, full relational operations, Database modifications, defining a relational schema in SQL, view definitions.

Simple queries in SQL, queries involving more than one relation, sub queries, full relational operations, Database modifications, defining a relational schema in SQL, view definitions.

Keys and foreign keys, constraints on attributes and tuples, modification of constraints, schema level constraints and Triggers.

Keys and foreign keys, constraints on attributes and tuples, modification of constraints, schema level constraints and Triggers.

Transaction Processing: Introduction to Transaction Processing, Transaction and System concepts, Desirable properties of Transactions, Characterizing schedules based on recoverability, Characterizing schedules based on Serializability, Transaction support in SQL.

Concurrency Control in Databases: Two-phase locking techniques for Concurrency control, Concurrency control based on Timestamp ordering, Multiversion Concurrency control techniques, Validation Concurrency control techniques, Granularity of Data items and Multiple Granularity Locking.

Recovery Concepts, NO-UNDO/REDO recovery based on Deferred update, Recovery techniques based on immediate update, Shadow paging, Database backup and recovery from catastrophic failures.

Introduction to OQL, Additional Forms of OQL Expressions, Object Assignment and Creation in OQL.

Transaction Processing: Introduction to Transaction Processing, Transaction and System concepts, Desirable properties of Transactions, Characterizing schedules based on recoverability, Characterizing schedules based on Serializability, Transaction support in SQL.

Concurrency Control in Databases: Two-phase locking techniques for Concurrency control, Concurrency control based on Timestamp ordering, Multiversion Concurrency control techniques, Validation Concurrency control techniques, Granularity of Data items and Multiple Granularity Locking.

Recovery Concepts, NO-UNDO/REDO recovery based on Deferred update, Recovery techniques based on immediate update, Shadow paging, Database backup and recovery from catastrophic failures.

Introduction to OQL, Additional Forms of OQL Expressions, Object Assignment and Creation in OQL.

1. Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

Course will give students a chance to use what they learn in the lectures, homework, SQL assignments, and a database implementation project.

CIA 50 %

ESE 50 %

Total Marks= 50

Course Description:

This course helps students to understand the basic concept of data structures for storing and retrieving ordered or unordered data. It describes the data structures including arrays, linked lists, binary trees, heaps, and hash tables.

Course Objective:

1. To analyse the asymptotic performance of algorithms

2. To demonstrate their familiarity with major data structures, rule to manipulate those, and their canonical applications

3. To construct efficient algorithms for some common computer engineering design problems

Time and Space complexity of algorithms, asymptotic analysis, average and worst case analysis, asymptotic notation, the importance of efficient algorithms.

The Queue ADT: Definition, Array representation of queue, Types of queues: Simple queue, circular queue, double ended queue (de-queue) priority queue, operations on all types of Queues. The List ADT: singly linked list implementation, insertion, deletion and searching operations on linear list, circular linked list implementation, Double linked list implementation, insertion, deletion and searching operations. Applications of Linked List.

Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap.

Insertion Sort, Selection sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting.

Introduction to Graphs, Definitions –DFS, BFS, Minimum Spanning Tree – Prim’s and Kruskal's Algorithm. Single-Source Shortest Paths – Bellman-Ford algorithm and Dijkstra’s Algorithm – – Applications of Graphs.

R2. NPTEL Course on “Data Structure and Algorithms Using JAVA” by Debasis Samanta, IIT Kharagpur.

R3. NPTEL Course on “Design and Analysis of Algorithms” by Madhavan Mukund, IIT Madras

1. Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

To introduce the student to Python programming & R programming concepts.

Introduction to Python: Basic Syntax, Data Types, Variables, Operators, Input/output, Flow of Control (Modules, Branching, Looping), Strings, Dictionaries, Lists, Function and Methods, Anonymous functions(Lambda, Map, List comprehension), Global and local variables, Concepts of Modules, Tuples, Object Oriented Python, Object Oriented Linux Environment, Exception Handling, Working with Pandas and Numpy, Working with beautiful soup, matplotlib, seaborn, ggplot, plotly, Load Images using pillow, Load audio files using scikit-learn(scipy.io), Connecting DB’s with Python, Creation of Python virtual Environment

Introduction & Installation of R, Data Objects- Data Types & Data Structures (e.g. lists. Arrays, matrices, data frames), Packages in R, Introduction to tidy verse (group of packages), Manipulating and Processing Data in R, Creating, Accessing and Sorting data frames, Extracting, Combining, Merging, reshaping data frames, Functions, Built in functions in R (numeric, character, statistical), Interactive reporting with R markdown, 

1. Python for Everybody Exploring Data using Python, Charles R. Severance, Shroff Publishers & Distributors, 1st Edition.

1. Introduction to Computer Science using Python, Charles/ Wiley

2. Python Power!: The Comprehensive Guide

3. Python Crash Course: A Hands-on, Project-Based Introduction to Programming

4. Beginning Programming with Python For DummiesLearning Python by: Fabrizio Romano

5. Python Projects by Laura Cassell , Alan Gauld / Wiley

6. Python Cookbook by David B. Brain K. Jones / Shroff / O'reilly Publisher

7. Head First Python by Paul Barry / Shroff / O'reilly Publisher

8. Professional Iron Python by John Paul Muller / Wiley India Pvt Ltd

9. Beginning Programming with Python for Dummies by John Paul Muller / Wiley India Pvt Ltd

ESE - 30 %

To reinforce knowledge of BigData Technologies such as Hadoop, Map reduce, HBase, PIG, Spark (PySpark) 

Research and Changing Nature of Data Repositories, Overlooked and Overrated Data Sharing, Curation of Scientific Data at Risk of Loss: Data Rescue And Dissemination, Introduction to Hadoop and its components, Hadoop Distributed File System (HDFS), HDFS Architecture, Hadoop Installation and Cluster Configuration, HDFS – Monitoring & Maintenance, Hadoop benchmarks, HDFS Data Storage Process, Hadoop Map Reduce paradigm, Partitioners and Combiners, Complex Map Reduce programming,ETL Process in Hadoop.

Overview of HBase: architecture, Installation, The HBaseAdmin and HBase Security, The Hive Data-ware House, Working with Hive QL, Data manipulation with Hive,Designing Data warehousing for an ETL Data Pipeline, Designing Data Lakes for an ETL Data Pipeline, Fundamentals of Airflow, Apache Spark APIs for large-scale data processing, Working with Kafka using Spark,Predictive Analysis.

2. Programming Hive by O’Rellay (Author:- Edward Capriolo, Dean Wampler, and Jason RutherglenEdward Capriolo, Dean Wampler, and Jason Rutherglen)

3. Hadoop The Definitive Guide 4 thEdition by O’Rellay (Author: - Tom White)

4. Hadoop In Practice by Manning (Author: - ALEX HOLMES)

5. Pro Hadoop by Aprss(Author:-Jason Venner)

6. Hadoop with python

7. Hadoop Real-World Solutions Cookbook by Packet publication (Author: Jonathan R. Owens, Jon Lentz,Brian Femiano)

8. Hadoop In Action by Manning Publications (Author: - CHUCK LAM)

9. Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

10. Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset

11. Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing

ESE - 30%

Course Description:

The Advanced Data Structures and Algorithms Lab is an intensive hands-on course designed to complement the theoretical understanding gained in the corresponding lecture-based course. Students will strengthen their practical skills in implementing and analysing complex data structures and algorithms. 

Course Objective:

1. To implement the basic concepts of linear and non-linear data structures.
2. To provide the students with various kinds of searching and sorting mechanisms.

To work with different tree traversal techniques. 

1. Write programs for implementing the following searching techniques. a. Linear search b. Binary search c. Fibonacci search.

2. Write programs for implementing the following sorting techniques to arrange a list of integers in ascending order. a. Bubble sort b. Insertion sort c. Selection sort d. Quick sort e. Merge sort.

3. Write programs to a. Design and implement Stack and its operations using List. b. Design and implement Queue and its operations using List.

4. Write programs for the following: a. Uses Stack operations to convert infix expression into postfix expression. b. Uses Stack operations for evaluating the postfix expression.

5. Write programs for the following operations on Single Linked List. (i) Creation (ii) insertion (iii) deletion (iv) traversal b. To store a polynomial expression in memory using single linked list.

6. Write programs for the following operations on Circular Linked List. (i) Creation (ii)insertion (iii) deletion (iv) traversal.

7. Write programs for the following: Uses functions to perform the following operations on Double Linked List. (i) Creation (ii) insertion (iii) deletion (iv) traversal in both ways..

8. Write a program to implement Stack using linked list.

9. Write a program to implement Linear Queue using linked list.

10. Write programs to implement graph traversal algorithms: a. Depth-first search. b. Breadth-first search.

11. Write a program to perform the following: a. Create a binary search tree. b. Traverse the above binary search tree recursively in pre-order, post-order and in-order. c. Count the number of nodes in the binary search tree.

R2. NPTEL Course on “Data Structure and Algorithms Using JAVA” by Debasis Samanta, IIT Kharagpur.

R3. NPTEL Course on “Design and Analysis of Algorithms” by Madhavan Mukund, IIT Madras.

CIA 50 %

ESE 50 %

Total Marks= 50

To introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research

 Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan

 Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan

 Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis.

 Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis.

 Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external.

Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools

 Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external.

Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools

 Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems

 Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables

2.      Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.

3.      Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

This course helps to understand computer-based vulnerabilities and explore different footprinting, reconnaissance, and scanning methods. The course also exposes the enumeration and vulnerability analysis methods and explores options for network protection.

1. To understand the basics of computer-based vulnerabilities.

2. To understand hacking options available in Web and wireless applications.

3. To practice tools to perform ethical hacking to expose the vulnerabilities.

Ethical Hacking Overview - Role of Security and Penetration Testers - Penetration-Testing Methodologies- Laws of the Land - Overview of TCP/IP- The Application Layer - The Transport Layer - The Internet Layer - IP Addressing. - Network and Computer Attacks - Malware - Protecting Against Malware Attacks.- Intruder Attacks - Addressing Physical Security

 Experiment 1: Install Kali or Backtrack Linux / Metasploitable/ Windows XP and basic operations.

Ethical Hacking Overview - Role of Security and Penetration Testers - Penetration-Testing Methodologies- Laws of the Land - Overview of TCP/IP- The Application Layer - The Transport Layer - The Internet Layer - IP Addressing. - Network and Computer Attacks - Malware - Protecting Against Malware Attacks.- Intruder Attacks - Addressing Physical Security

 Experiment 1: Install Kali or Backtrack Linux / Metasploitable/ Windows XP and basic operations.

Footprinting Concepts - Footprinting through Search Engines, Web Services, Social Networking Sites, Websites, and Email - Competitive Intelligence - Footprinting through Social Engineering - Footprinting Tools - Network Scanning Concepts - Port-Scanning Tools - Scanning Techniques - Scanning Beyond IDS and Firewall.

Experiment 2: Practice the basics of reconnaissance.

Experiment 3: Using FOCA / SearchDiggity tools, extract metadata and expand the target list.

Experiment 4: Aggregates information from public databases using online free tools like Paterva’s Maltego.

Experiment 5: Scan the target using tools like Nessus.

Footprinting Concepts - Footprinting through Search Engines, Web Services, Social Networking Sites, Websites, and Email - Competitive Intelligence - Footprinting through Social Engineering - Footprinting Tools - Network Scanning Concepts - Port-Scanning Tools - Scanning Techniques - Scanning Beyond IDS and Firewall.

Experiment 2: Practice the basics of reconnaissance.

Experiment 3: Using FOCA / SearchDiggity tools, extract metadata and expand the target list.

Experiment 4: Aggregates information from public databases using online free tools like Paterva’s Maltego.

Experiment 5: Scan the target using tools like Nessus.

Enumeration Concepts - NetBIOS Enumeration – SNMP, LDAP, NTP, SMTP, and DNS Enumeration - Vulnerability Assessment Concepts - Desktop and Server OS Vulnerabilities - Windows OS Vulnerabilities - Tools for Identifying Vulnerabilities in Windows- Linux OS Vulnerabilities- Vulnerabilities of Embedded Oss

Experiment 6: Automate dig for vulnerabilities and match exploits using Armitage

Enumeration Concepts - NetBIOS Enumeration – SNMP, LDAP, NTP, SMTP, and DNS Enumeration - Vulnerability Assessment Concepts - Desktop and Server OS Vulnerabilities - Windows OS Vulnerabilities - Tools for Identifying Vulnerabilities in Windows- Linux OS Vulnerabilities- Vulnerabilities of Embedded Oss

Experiment 6: Automate dig for vulnerabilities and match exploits using Armitage

Access Control Lists. - Cisco Adaptive Security Appliance Firewall - Configuration and Risk Analysis Tools for Firewalls and Routers - Intrusion Detection and Prevention Systems – Network-Based and Host-Based IDSs and IPSs - Web Filtering - Security Incident Response Teams – Honeypots.

 Experiment 7: View and capture network traffic using Wireshark.

Access Control Lists. - Cisco Adaptive Security Appliance Firewall - Configuration and Risk Analysis Tools for Firewalls and Routers - Intrusion Detection and Prevention Systems – Network-Based and Host-Based IDSs and IPSs - Web Filtering - Security Incident Response Teams – Honeypots.

 Experiment 7: View and capture network traffic using Wireshark.

To reinforce knowledge of BigData Technologies such as Hadoop, Map reduce, HBase, PIG, Spark (PySpark) 

Research and Changing Nature of Data Repositories, Overlooked and Overrated Data Sharing, Curation of Scientific Data at Risk of Loss: Data Rescue And Dissemination, Introduction to Hadoop and its components, Hadoop Distributed File System (HDFS), HDFS Architecture, Hadoop Installation and Cluster Configuration, HDFS – Monitoring & Maintenance, Hadoop benchmarks, HDFS Data Storage Process, Hadoop Map Reduce paradigm, Partitioners and Combiners, Complex Map Reduce programming,ETL Process in Hadoop.

Overview of HBase: architecture, Installation, The HBaseAdmin and HBase Security, The Hive Data-ware House, Working with Hive QL, Data manipulation with Hive,Designing Data warehousing for an ETL Data Pipeline, Designing Data Lakes for an ETL Data Pipeline, Fundamentals of Airflow, Apache Spark APIs for large-scale data processing, Working with Kafka using Spark,Predictive Analysis.

2. Programming Hive by O’Rellay (Author:- Edward Capriolo, Dean Wampler, and Jason RutherglenEdward Capriolo, Dean Wampler, and Jason Rutherglen)

3. Hadoop The Definitive Guide 4 thEdition by O’Rellay (Author: - Tom White)

4. Hadoop In Practice by Manning (Author: - ALEX HOLMES)

5. Pro Hadoop by Aprss(Author:-Jason Venner)

6. Hadoop with python

7. Hadoop Real-World Solutions Cookbook by Packet publication (Author: Jonathan R. Owens, Jon Lentz,Brian Femiano)

8. Hadoop In Action by Manning Publications (Author: - CHUCK LAM)

9. Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

10. Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset

11. Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing

To introduce students in Data Analytics, Visualization and Reporting

Business Intelligence basic, Information gathering, Decision making, Managing BI, BI User Segmentation, Gathering BI Requirements, Content and Knowledge Management, Strategic Approach to BI, Significance of visual analytics Information Visualization,  Data Representation, Data collection and binding: Structured Data, Unstructured data, MS EXCEL: Functions, Formula, Charts, Pivots and Lookups, Data Analysis Tool pack-Descriptive Summaries, Correlation, Regression, Data analytics Life Cycle: Discovery, Data preparation, Model planning, Model building implementation, Quality assurance, Documentation, Management approval, Installation, Acceptance and operation, Introduction to Tableau: Intelligent data analysis, Nature of Data, Analytics Processes and tools, Analysis vs. Reporting, Modern Data Analytic Tools, Data sources in Tableau, Visualization Algorithms: Visual Encodings, Taxonomy of data visualization, Choosing appropriate visuals, Applying calculations using functions, statistics, Data sorting, filters, Interactive visualization, Dashboard Design

2. Designing Data Visualizations, by Steele,O'Reilly

3. Tableau your data, by Daniel G/ Wiley

4. Graphs Cookbook, Hrishi V. Mittal, Packt Publishing

5. Python Data Visualization Cookbook,Igor Milovanović, Packt Publishing

6. Learning Python Data Visualization, Chad Adams, Packt Publishing

7. Data Visualization with D3.js Cookbook,Nick Qui Zhu,Packt Publishing

8. Getting Started with D3,Mike Dewar,O'Reilly 9. Data Visualization with JavaScript

10. Data Visualization for Dummies

11. High Impact Data Visualization with Power View, Power Map, and Power BI

12. The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions

13. Mastering Tableau 2021:- by Marleen Meie

Course Description:   

This course elaborates on the basics of business intelligence concepts and the knowledge delivery. Students shall also examine the efficacy and the business applications in real world.  

Course Objective:

1. Be exposed with the basic rudiments of business intelligence system

2. Understand the modeling aspects behind Business Intelligence

3. Understand of the business intelligence life cycle and the techniques used in it 

Effective and timely decisions – Data, information and knowledge – Role of mathematical models – Business intelligence architectures: Cycle of a business intelligence analysis – Enabling factors in business intelligence projects – Development of a business intelligence system – Ethics and business intelligence.

The business intelligence user types, Standard reports, Interactive Analysis and Ad Hoc Querying, Parameterized Reports and Self-Service Reporting, dimensional analysis, Alerts/Notifications, Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards, Geographic Visualization, Integrated Analytics, Considerations: Optimizing the Presentation for the Right Message.

Efficiency measures – The CCR model: Definition of target objectives- Peer groups – Identification of good operating practices; cross efficiency analysis – virtual inputs and outputs – Other models. Pattern matching – cluster analysis, outlier analysis

Marketing models – Logistic and Production models – Case studies.

Future of business intelligence – Emerging Technologies, Machine Learning, Predicting the Future, BI Search & Text Analytics – Advanced Visualization – Rich Report, Future beyond Technology.

T2. Carlo Vercellis, “Business Intelligence: Data Mining and Optimization for Decision Making”, Wiley Publications, 2009.

ESE - 50

Course Description:

This course provides the concepts and algorithms in machine learning and the methods to apply them in real time problems. 

Course Objective:

1.To gain knowledge in the basics of Python Machine Learning Libraries.

2.To analyze the applicability, benefits, and drawbacks of the Supervised and Unsupervised machine learning techniques.

3.To acquire knowledge on the recommendation systems and their working methodology 

Need for Machine Learning, Machine Learning in relation to other fields, Types of Machine Learning, Challenges of Machine Learning, Machine Learning Process and Applications, Data, Data Analytics, Descriptive Statistics, Univariate, Bivariate and Multivariate Data, Feature Engineering, Dimensionality Reduction techniques, Learning and its Types.

Introduction to Similarity based Learning, Nearest Neighbor Learning, Weighted K-Nearest Neighbor Algorithm, Nearest Centroid Classifier, Locally weighted Regression, Introduction to Regression, Linearity, Correlation and Causation, Linear Regression, Multiple Linear Regression

Experiment 1: Implementation of Linear Regression, KNN Algorithm

Decision Tree learning Model, Decision Tree Induction Algorithms: ID3 Tree Construction, C4.5 Construction, Classification and Regression Trees Construction, Regression Trees, Validating and Pruning of Decision Trees. Probability based Learning, Bayes Theorem, Classification using Bayes Model, Naïve Bayes Algorithm for Continuous Attributes, Other Naïve Bayes Classifiers.

Experiment 2: Implementation of Decision Tree and Naïve Bayes Algorithm

Introduction to Clustering Approaches, Proximity Measures, Hierarchical Clustering Algorithms, Partitional Clustering Algorithm, Overview of Reinforcement Learning, Components, Markov Decision Process.

Experiment 3: Implementation of Decision Clustering Algorithm

Experiment 4: Implementation of Reinforcement Algorithm

Commands and Syntax, Packages and Libraries, Introduction to Data Types, Data Structures in R, Importing and Exporting Data.

Data exploration, Qualitative and Quantitative Data, Measure of Central Tendency (Mean, Median and Mode), Measure of Positions (Quartiles, Deciles, Percentiles and Quantiles), Measure of Dispersion (Range, Median, Absolute deviation about median, Variance and Standard deviation), Other Measures: Quartile and Percentile, Interquartile Range.

Experiment 5: Case study on Data Visualization for any Application

T2. Brett Lantz, “Machine Learning with R: Expert techniques for predictive modeling”, 3rd Edition, Packt Publishing, 2019. ISBN : 978-1788295864.

R2. David Forsyth, “Applied Machine Learning”, Springer, 2019.

R3. M.Gopal, “Applied Machine Learning”, McGraw-Hill Education, 1st edition, 2019.

ESE - 50

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Project Work

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

To have the better understanding of:

1. To expose the students to the prevalent IoT architecture and the associated hardware and software technology.

2. To enable the students to develop full-fledged IoT systems leveraging the state-of-the-art computing environments.

Introduction, Architectures, Applications, Devices, Security and privacy

Introduction to generic IoT architecture, Protocols, Standards, Databases, Time Bases, IoT Devices

The IoT Architectural Reference Model as Enabler, Guidance to the ARM,

IoT Reference Model, IoT Reference Architecture, The IoT ARM Reference

Manual, Toward a Concrete Architecture

Standards, Interoperability, Discoverability, Security and Privacy in IoT

Cloud computing, Big data, Big-stream-oriented Architecture, Performance

Evaluation, Fog Computing and the IoT, Virtualization agnd Replication,

The IoT Hub, Operational Scenarios, Synchronization Protocol.

Hardware for the IoT , Classes of Constrained Devices, Hardware Platforms, Software for the IoT, Networking, Programming Model, Integration Challenges, Implementation and Validation.

2.      Bassi, Alessandro, et al. Enabling things to talk. Springer Nature, 2013.

3.      Cirani, Simone, et al. Internet of things: architectures, protocols and standards. John Wiley & Sons, 2018.

2.      Lea, Perry. Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security. Packt Publishing Ltd, 2018.

3.      Buyya, Rajkumar, and Satish Narayana Srirama, eds. Fog and edge computing: principles and paradigms. John Wiley & Sons, 2019.

Course Description:

Students need to learn from textbooks, lectures, and other study materials to build a strong foundation. But if you want them to understand the real business world, you need to give them a taste of the practical side of things. So, industrial learning is a big part of managerial studies. It gives students hands-on experience and helps them figure out where they want to work in the organization as a whole. As a result, the department offers summer internships at well-known companies that give students an all-around look at the business world for one month. At the end of their second semester, students take part in this internship. This chance also helps lay the groundwork for the next semester's placement season.

Course Objective:

1. To perform a task involving research or design, that is carefully planned to achieve a particular aim.

2. To learn modular programming - analyze problems, design solutions, learn new tools and implement the system as a team/ individual.

1.The student shall undergo an Internship for 30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  7th semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Assessment of Project Work(Phase I)

▪Continuous Internal Assessment:100 Marks

♦Presentation assessed by Panel Members

♦Guide

♦Mid semester Project Report

End Semester Review - 100 Marks

This course aims at making the students get an understanding of the compression techniques available for multimedia applications and also get an understanding of the encryption that can be implemented along with the compression.

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

2. David Salomon : Data Compression – The Complete Reference, Springer Verlag New York Inc., 4th Edition, 2006

3. Yun Q.Shi, HuifangSun : Image and Video Compression for Multimedia Engineering -  Fundamentals, Algorithms & Standards, CRC press, 2008

4.Jan Vozer : Video Compression for Multimedia, AP Profes, NewYork, 1995.

5. William Stallings, “Cryptography and Network Security”, 6th. Ed, Prentice Hall of India, New Delhi ,2013

6. William Stallings, “Network Security Essentials”, 5^thed. Prentice Hall of India, New Delhi

ESE-50

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Project Work 

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

▪ Dissertation (Exclusive assessment of Project Report): 100 Marks

♦ Internal Review : 50 Marks

♦ External review : 50 Marks