School of Computer Science and Engineering

CURRICULUM AND SYLLABI (2019-2020)

M.Tech (CSE) - Specialization in Data Science – 5 year Integrated

VISION STATEMENT OF VELLORE INSTITUTE OF TECHNOLOGY

Transforming life through excellence in education and research.

MISSION STATEMENT OF VELLORE INSTITUTE OF TECHNOLOGY

World class Education: Excellence in education, grounded in ethics and

critical thinking, for improvement of life. Cutting edge Research: An innovation ecosystem to extend knowledge and

solve critical problems. Impactful People: Happy, accountable, caring and effective workforce and

students. Rewarding Co-creations: Active collaboration with national & international

industries & universities for productivity and economic development. Service to Society: Service to the region and world through knowledge and

compassion.

VISION STATEMENT OF THE SCHOOL OF COMPUTER SCIENCE AND ENGINEERING To be a world-renowned centre of education, research and service in computing and allied domains.

MISSION STATEMENT OF THE SCHOOL OF COMPUTER SCIENCE AND ENGINEERING

• To offer computing education programs with the goal that the students become technically competent and develop lifelong learning skill.

• To foster vibrant outreach programs for industry, research organizations, academia and society.

• To undertake path-breaking research that creates new computing technologies and solutions for industry and society at large.

School of Computer Science and Engineering

2. Graduates will use suitable principle, hypothesis, mathematics and computational technology

to analyze and solve problems encountered in the applications of computer systems.

3. Graduates will own a professional attitude as an individual or a team member with

contemplation for society, professional ethics, environmental factors and motivation for lifelong

learning.

4. Graduates will communicate, using oral, written and computer based communication

technology, as well as function effectively as an individual and a team member in professional

environment.

5. Graduates will realise the local, national and global issues related to the growth and

applications of computer systems and to be solicitous of the impact of these issues on different

cultures.

1. Graduate will acquire fundamental knowledge and expertise essential for professional

practice in computer engineering.

M.Tech (CSE) - Specialization in Data Science – 5 year Integrated

PROGRAMME EDUCATIONAL OBJECTIVES (PEOs)

M. Tech Computer Science and Engineering Specialization in Data Science 5-Year Integrated

PROGRAMME OUTCOMES (POs) PO_01: Having an ability to apply mathematics and science in engineering applications.

PO_02: Having a clear understanding of the subject related concepts and of contemporary issues and apply them to identify, formulate and analyze complex engineering problems.

PO_03: Having an ability to design a component or a product applying all the relevant standards and with realistic constraints, including public health, safety, culture, society and environment

PO_04: Having an ability to design and conduct experiments, as well as to analyze and interpret data, and synthesis of information

PO_05: Having an ability to use techniques, skills, resources and modern engineering and IT tools necessary for engineering practice

PO_06: Having problem solving ability- to assess social issues (societal, health, safety, legal and cultural) and engineering problems

PO_07: Having adaptive thinking and adaptability in relation to environmental context and sustainable development

PO_08: Having a clear understanding of professional and ethical responsibility

PO_09: Having cross cultural competency exhibited by working as a member or in teams

PO_10: Having a good working knowledge of communicating in English – communication with engineering community and society

PO_11: Having a good cognitive load management skills related to project management and finance

PO_12: Having interest and recognize the need for independent and lifelong learning

M. Tech Computer Science and Engineering Specialization in Data Science 5-Year Integrated

ADDITIONAL PROGRAMME OUTCOMES (APOs)

APO_01: Having an ability to be socially intelligent with good SIQ (Social Intelligence Quotient) and EQ (Emotional Quotient)

APO_02: Having Sense-Making Skills of creating unique insights in what is being seen or observed (Higher level thinking skills which cannot be codified)

APO_03: Having design thinking capability

APO_04: Having computational thinking (Ability to translate vast data in to abstract concepts and to understand database reasoning

APO_05: Having Virtual Collaborating ability

APO_06: Having an ability to use the social media effectively for productive use

APO_07: Having critical thinking and innovative skills

APO_08: Having a good digital footprint

School of Computer Science and Engineering Year of Commencement: 2013

3. Evaluate the use of data from acquisition through cleansing, warehousing, analytics,

and visualization to the ultimate business decision.

2. Design storage structures to represent huge data and apply artificial statistics and

computational analysis for data to predict and represent knowledge.

1. Employ mathematical models with indispensable engineering and scientific

principles to unravel solutions for life problems using appropriate data structures and

algorithms.

4. Utilize the core concepts of computer science and engage in research methods to

interpret, process, experiment and conclude the investigations.

M.Tech (CSE) - Specialization in Data Science – 5 year Integrated

PROGRAMME SPECIFIC OUTCOMES (PSOs)

SCHOOL OF COMPUTER SCIENCE AND ENGINEERING M.Tech Computer Science and Engineering Spl. in Data Science –

5 Year Integrated

Curriculum – AY (2019 – 2020)

Sl.NO

Category

Total No. of Credits (2019 Batch)

1 University Core (UC) 61 2 Programme Core (PC) 85 3 University Elective (UE) 12 4 Programme Elective (PE) 62

Total 220

University Core [ 61 Credits]

L – Lecture T- Tutorial P- Practical J- Project C- Credits E –Engineering S – Science H – Humanities M - Management

Sl.No Subject Code Course Title L T P J C Pre-Req Category

1. CHY1002 Environmental Sciences 0 0 0 0 0 - S

2. CHY1701 Engineering Chemistry 3 0 2 0 4 - S

3. CSE1001 Problem Solving and Programming 0 0 6 0 3 - E

4. CSE1002 Problem Solving and Object Oriented Programming

0 0 6 0 3 - E

5.

ENG1901/1902 /1903

English 0 0 2 4 2 A Pass in VIT EPT or ENG1002

H

6. HUM1021 Ethics and Values 2 0 0 0 2 - H

7. MAT1011 Calculus for Engineers 3 0 2 0 4 - S

8. MAT2001 Statistics for Engineers 2 2 2 0 4 MAT1011 S

9. MGT1022 Lean Start-up Management 1 0 0 4 2 - M

10. PHY1701 Engineering Physics 3 0 2 0 4 - S

11. PHY1999 Introduction to Innovative Projects 1 0 0 0 1 - S

12. CSI3999 Technical Answers for Real World Problems (TARP)

1 0 0 8 2 PHY1999 E

13. CSI4098 Comprehensive Examination 0 0 0 0 1 - E

14. CSI4099 Co-op/Capstone Project 0 0 0 0 18 - E

15. EXC4097 Co-Extra Curricular Basket 0 0 0 0 0 - M

16. FLC4097 Foreign Language Course Basket 0 0 0 0 2 - H

17. STS5097 Soft Skills 0 0 0 0 8 - H 18. CSI3099 Industrial Internship 0 0 0 0 1 - E

19. ENG1002 Effective English (Bridge Course)

0 0 4 0 0 - H

Total

61 Credits

Program Core (85 credits) (CSE - 71 + DS – 14 credits)

CSE Core [71 credits]

Sl.No

Course Code

Course Title

L

T

P

J

C

Pre-

Requisite

Category

1 MAT2002 Applications of Differential and Difference Equations 3 0 2 0 4 MAT1011 S

2 MAT1014

Discrete Mathematics and Graph Theory

3

1

0

0

4

-

S

3 MAT1022

Linear Algebra

3

0

0

0

3

MAT2002

S

4 EEE1001

Fundamentals of Electrical and Electronics Engineering

2

0

2

0

3

-

E

5 CSE1003

Digital Logic and Design

3

0

2

0

4

-

E

6 CSIxxx Advanced Algorithms 2 0 2 0 3 - E

7 CSI1013

Programming in Java

3

0

2

0

4 -

E

8 CSI1004 Computer Organization and Architecture 3 0 0 0 3 - E

9 CSE2003

Data Structures and Algorithms

3

0

2

0

4

-

E

10

CSI1001 Principles of Database Systems 2 0 2 0 3 - E

11 CSI1006 Advanced Database Management Systems

3 0 0 0 3 - E

12

CSI1007 Software Engineering Principles 2 0 2 0 3 - E

13 CSI1003 Formal Languages and Automata Theory 3 0 0 0 3 - E

14 CSI1008 Principles of Compiler Design 3 0 0 0 3 - E

15 CSI1002 Operating System Principles 2 0 2 0 3 - E

16. CSI1009 Cloud Computing Methodologies 3 0 2 0 4 - E

17 MDI1001 Advances in Web Technologies 3 0 2 0 4 - E

18

CSI1010 Microprocessor and Interfacing Techniques

2 0 2 0 3 - E

19 CSI1011 Data Communication and Networks 3 0 2 0 4 - E

20 CSI1012 Applied Cryptography and Network Security

2 0 2 0 3 - E

21 CSI1014 Artificial Intelligence and Expert Systems

3 0 0 0 3 - E

Total 71 credits

Data Science Core - 14 credits

Sl.No Course Title L T P J C Pre-Req

Category

1. MDI1002 Foundations of Data Science 3 0 0 0 3 - E

2. CSI1015 Data Science Programming 2 0 2 0 3 - E

3. MDI1003 Machine Learning for Data Science

3 0 2 0 4 - E

4. CSI1016 Advanced Data Visualization Techniques

3 0 2 0 4 - E

Total 14 Credits

LIST OF PROGRAM ELECTIVES (62 Credits)

CSE Electives (Minimum 33 Credits) Sl.No Course Title L T P J C Pre-

Req Category

1. CSI1017 Internetworking with TCP/IP 3 0 0 0 3 - E 2. CSI1018 Natural Language Processing and

Computational Linguistics 3 0 0 4 4 - S

3. CSI1019 Logic and Combinatorics for Computer Science

3 0 0 0 3 - E

4. CSI1020 Computer Graphics and Multimedia

3 0 2 0 4 - E

5. CSI1021 Computer Oriented Numerical Methods

3 0 2 0 4 - E

6. MDI1004 Advanced Java 2 0 2 0 3 CSI1013 E

7. CSI1022 Distributed Systems 3 0 2 0 4 - E

8. CSI1023 Text Mining 3 0 0 0 3 - E

9. CSI1024 Internet of Everything 3 0 2 0 4 - E

10. CSI1025 Soft Computing Techniques 3 0 0 4 4 - E

11. CSI1026 Advanced Wireless Networks 3 0 2 0 4 - E 12. CSI1027 Augmented Reality and Virtual

Reality 3 0 0 4 4 - E

13. CSI1028 Blockchain Technologies 3 0 0 4 4 - E 14. CSI1029 Quantum Computing Techniques 3 0 0 0 3 - E 15. CSI1030 Software Verification and

Validation 3 0 0 0 3 - E

16. CSI1031 Advanced Computer Architecture 3 0 0 0 3 CSI1004 E

17. CSI1032 Advances in Pervasive Computing

3 0 0 0 3 - E

18. CSI1033 Game Theory

3 0 0 0 3 - E

19. CSI1034 GPU Programming

3 0 0 0 3 - E

20. CSI1005 User Interface Design

3 0 0 0 3 - E

21. CSI1035 Python Programming 2 0 2 0 3 - E

22. CSI1036 Advanced Data Compression Techniques

3 0 0 0 3 - E

23. CSI1037 Programming Paradigms 3 0 2 0 4 - E

24. CSI1038 Cyber Security and Application Security

3 0 2 0 4 - E

25. CSI1039 Advanced Graph Algorithms 3 0 0 0 3 - E

26. CSI1040 Software Project Management 3

0

0

0

3 - E

27. CSI1041 Robotics: Machines and Controls

3

0

0

0

3 -

E

28. CSI1042 Mathematical Modelling and Simulation

3 0 0 0 3 - E

Data Science Electives (Minimum 18 Credits)

Sl.No Course Title L T P J C Pre-

Req Category

1. CSI1043 Advanced Predictive Analytics 3 0 2 0 4 - E

2. CSI1044 Data warehousing and Data Mining

3 0 2 0 4 - E

3. MDI1006 Business Intelligence 3 1 0 0 4 - E

4. MDI1007 Intelligent Database Systems 3 0 0 4 4 - E

5. MDI1008 Medical Informatics 3 0 0 0 3 - E

6. MDI1009 Statistical Inference and Modelling

3 0 2 0 4 - E

7. MDI010 Advances in Data Engineering 3 0 0 4 4 - E

8. MDI1011 Knowledge Engineering and Management

3 0 0 4 4 - E

9. MDI1012 Image and Video Analytics 3 0 0 4 4 - E 10. CSI1045 Cognitive Science and Decision

making 3 0 0 0 3 - E

11. CSI1046 Advances in Database Administration and Security

3 0 0 0 3 - E 12. MDI1013 Advanced Data Analytics 3 0 0 0 3 - E

13. MDI1014 Bayesian Statistical Methods 3 0 0 4 4 - E 14. MDI1015 Neural Networks and Deep

Learning 3 0 0 0 3 - E

15. MDI1016 Nature Inspired Optimization Techniques

3 1 0 0 4 - E

16. MDI1017 Statistics and Exploratory Analytics

3 0 0 0 3 - E

17. CSI1047 Web mining and Social Network Analysis

3 0 0 4 4 - E

Notation: L-Lecture, T-Tutorial, P-Practical, J-Project, C-Credits

University Elective [12 Credits]