Syllabus for
MTech (Data Science)
Academic Year  (2023)

Assessment is based on the performance of the student throughout the semester.

Assessment of each paper

• Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

• End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Department of Computer Science and Engineering started of journey in the year 2009 to produce qualified Engineers for the society with variety of skills. The department offers the following degrees Bachelor of Technology, Master of Technology, and Doctor of Philosophy in the areas of Computer Science and Engineering and Information Technology. Over the decade the department has inducted faculties to collectively pooled resources who can train the students in 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 from its inception has been keen on setting up labs for the students at present the labs infrastructure for the students are Tannenbaum lab, High Performance computing Lab, Bring your Own Device lab, Machine learning lab, CISCO Networking Lab, Red Hat Linux lab, specifically, for the students to be prepared for the lab curriculum and their research.

The department periodically conducts hands-on workshop on recent technology like Internet of Things, Cloud computing, Machine learning so that the students are connected with current and technologies. The department imparts teaching in Holistic method, where students who are trained under holistic education will be better citizens of Nation. The main educational goal is to prepare students for research and career in industry or in universities. 

Department Vision

“To fortify Ethical Computational Excellence”

Department Mission

1.      Imparts core and contemporary knowledge in the areas of Computation and Information Technology

2.      Promotes the culture of research and facilitates higher studies

3.      Acquaints the students with the latest  industrial practices, team building and entrepreneurship

4.      Sensitizes the students to serve for environmental, social & ethical needs of society through lifelong learning.

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 educations 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 the experienced academic and industrial expertise, by considering current as well as future demands of enterprises.  By looking at the multidisciplinary nature of data science, the curriculum offers many interdisciplinary subjects and also encourages students to do their Dissertation in a multidisciplinary environment.  The programme enables the students to apply the knowledge of data science and computer science in the field of natural language processing, Big data as well as many emerging technologies for solving the real world problems encountered during day-to-day life. Students will get a good exposure to interpret, manage as well as evaluate the large amount of heterogeneous data in the real time environment. In addition to this the department offers  a dedicated research centre as well as specialized labs for this program. During the Dissertation phase, students are encouraged to do their research in this specialized lab under the supervision of a dedicated supervisor or in the industries to make them industry or research ready. The programme consists of the modules to be learnt as compulsory electives along with core subjects of data science as well as computer science. Few of them include:

•    Advanced Database Management systems
•    Advance artificial intelligence
•    Advance Data Mining
•    Statistical foundation for data science
•    Big Data analytics
•    Machine Learning.
•    Data Visualisation Techniques
•    Massive graph analysis
•    Scientific Computing
•    Matrix Computations
•    Predictive analytics
•    Image and Video Analysis
•    Bioinformatics

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

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

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