M. Tech Specialisations and Curriculum

M.Tech Specialisations and Curriculum

Specialisations: The M.Tech in Data Science at Walchand College of Engineering (WCE), offered by the IT Department, provides a strong foundation in advanced knowledge of data science field, modern data technologies, and research methodologies. It focuses on areas like artificial Intelligence, machine learning, data analysis/analytics, Data visualization and mathematics. It provides various tracks for specialization using Professional elective Bucket list. The program prepares students for roles in research, academia, and industry.

Curriculum – The M.Tech curriculum is designed to provide students with advanced knowledge and skills in their chosen specialisation. The curriculum is designed to provide students with a comprehensive education in Data science and engineering. M.Tech (Data Science) curriculum is started in 2024-2025 and it is designed as per  NEP 2020 policy.

Program Educational Objectives (PEOs)

Post graduates on successful completion of the programme in Data Science will be able to:

  • PEO 1:  Contribute individually and in a team to carry out data collection and processing for the development of Data Science methodologies with professional skills.

  • PEO 2:  Apply domain expertise to interpret data and demonstrate technical competency in handling real life projects by analysing, evaluating and synthesizing the information.

  • PEO 3: Exhibit continuous learning attitude, ethical behaviour and techno-socio responsibilities as an expert in Data Science field.

Program Outcomes (POs)

On successful completion of the programme of post-graduation in Data Science, students will be able to:

  • PO1: Apply appropriate data handling skills and techniques for development of practical solutions in the domain of Data Science (Research Skill)

  • PO2: Demonstrate comprehensive understanding of Data Science theories, knowledge, principles, methodologies, and tools to address complex data challenges (Scholarship of Knowledge)

  • PO3: Conceptualize and solve data engineering problems to find feasible and optimal solutions (Critical Thinking and problem solving)

  • PO4: Communicate research work to the scientific and societal community through technical reports, publications and IPR’s (Communication)

  • PO5: Exhibit ethical and professional code of conduct for life-long learning and providing sustainable solution on societal issues (Ethical Practices, Social responsibility, Managerial skills and Life-long learning)

  • PO6: Recommend suitable mechanisms including data handling, processing, programming, visualising, transforming, interpreting, analysing, storing, managing and securing data for scalable data driven applications (Program Specific Outcome)

Structure

Tabular form of Program Structure ( Link to be provided for detailed note)

Credit System for F. Y. M. Tech. (Data Science) SEM-I AY 2024-25

Sr. No.

Category

Course Code

Course Name

L

T

P

Hrs.

Cr

MSE/ LA1

ISE/ LA2

ESE

Remark

Professional Core (Theory)

1

PC

7IC501

Research Methodology and IPR

3

0

0

3

3

30

20

50

 

2

PC

1DS501

Mathematics for Data Science

3

0

0

3

3

30

20

50

 

3

PC

1DS502

Data Structures and Algorithms

3

0

0

3

3

30

20

50

 

4

PC

1DS503

Principles of  Database Systems

2

0

0

2

2

30

20

50

 

Professional Core (Lab)

5

PC

1DS551

Data Structures and Algorithms Lab

0

0

2

2

1

30

30

40

 

6

PC

1DS552

Python Programming Lab

0

0

2

2

1

30

30

40

 

7

PC

1DS553

Logical Programming for Data Science

1

0

2

2

2

30

30

40

 

Professional Elective (Theory)

8

PE

Refer list

Professional Elective 1

3

0

0

3

3

30

20

50

 

9

PE

Refer list

Professional Elective 2

3

0

0

3

3

30

20

50

 
 

Total

18

0

6

23

21

 

 

(All below courses are the elective courses for SEM I and II) Students can choose any 4 courses from the following tracks/electives; at least one course from each level
Professional Elective Course List for F. Y. M. Tech. (Data Science) (SEM-I+ II) AY 2024-25

Sr. No.

Elective course name

Code

Level

T1-Mathematical Data Analysis

T2-Data Modelling

T3-Data Science Applications

1

Statistical Inference 

1DS511

1

YES

NO

YES

2

Time Series Data Analysis

1DS512

1

YES

YES

YES

3

Multi‐Criteria Decision Making

1DS513

1

YES

YES

YES

4

Data Modelling and Simulation

1DS514

1

YES

YES

YES

5

Data‐driven Analytics

1DS515

2

YES

YES

NO

6

AIML in Data Science

1DS516

2

YES

YES

YES

7

Numerical Optimization in Data Science

1DS517

2

YES

YES

YES

8

Graph Theory in Data Science

1DS518

2

YES

YES

NO

9

Pattern Recognition

1DS519

3

YES

YES

YES

10

Financial Data Science 

1DS520

3

NO

YES

YES

11

Social Data Analysis

1DS531

3

NO

YES

YES

12

Data Science in Businesses

1DS532

3

YES

YES

YES

13

Game theory

1DS533

3

YES

YES

YES

    

11

12

11

Credit System for F.Y. M.Tech. (Data Science) SEM-II AY 2024-25

Sr. No.

Category

Course Code

Course Name

L

T

P

Hrs.

Cr

MSE/ LA1

ISE/ LA2

ESE

Remark

Professional Core (Theory)

1

PC

1DS521

Data Mining and Warehousing

3

0

0

3

3

30

20

50

 

2

PC

1DS522

Data Handling and Visualization

2

0

0

2

2

30

20

50

 

3

PC

1DS523

Multidimensional Data Analysis

3

0

0

3

3

30

20

50

 
 

4

PC

1DS571

Data Mining and Warehousing Lab

0

0

2

2

1

30

30

40

 

5

PC

1DS572

Data Handling and Visualization Lab

0

0

2

2

1

30

30

40

 

6

PC

1DS573

Multidimensional Data Analysis Lab

0

0

2

2

1

30

30

40

 

7

PC

1DS574

Seminar

0

0

2

2

1

30

30

40

 

Professional Elective (Theory)

8

PE

Refer List

Professional Elective 3

3

0

0

3

3

30

20

50

 

9

PE

Refer List

Professional Elective 4

3

0

0

3

3

30

20

50

 

Open Elective

10

OE

Refer List

Open Elective

3

0

0

3

3

30

20

50

 
 

Total

17

0

8

25

21

 
Credit System for S. Y. M. Tech. (Computer science & Information and technology) SEM-III AY 2024-25

Sr. No.

Category

Course Code

Course Name

L

T

P

Hrs.

Cr

MSE/ LA1

ISE/ LA2

ESE

Remark

Professional Core (Theory)

1

PR

7IT691

 Dissertation Phase-I

0

0

24

24

12

30

30

40

POE

2

PE

Refer List

Online/NPTEL/ Swayam Course

3

0

0

3

3

0

25

75

 

3

PE

Refer List

Online/NPTEL/ Swayam Course

3

0

0

3

3

0

25

75

 
 
 

Total

6

0

24

30

18

 

 

NPTEL Course List

Sr. No.

Course Code

Name of NPTEL courses

Link

Institute

1

 7IT611

Data Science for Engineers, Prof. Shanka, Rengasamy

https://nptel.ac.in/courses/106106179                

IIT Madras             

2

 7IT612

Deep Learning-Prof. S. Iyengar, Sukrit Gupta

https://nptel.ac.in/courses/106106184  

IIT Ropar, IIT Madras

3

 7IT613

Introduction to Machine Learning, Dr. B. Ravindran

https://nptel.ac.in/courses/106106139

IIT Madras

4

7IT 614

Cloud Computing-  Prof. Soumya Kanti Ghosh

https://nptel.ac.in/courses/106105167

IIT Kharagpur

Credit System for S. Y. M. Tech. (Computer science & Information and technology) SEM-IV AY 2024-25

Sr. No.

Category

Course Code

Course Name

L

T

P

Hrs.

Cr

MSE/ LA1

ISE/ LA2

ESE

Remark

Professional Core (Theory)

1

PR

7IT692

Dissertation Phase-II

0

0

34

34

 17

30

30

40

POE

2

PC

7IT645

Internship

0

0

4

4

2

0

0

100

 

3

PC

7IT646

Techno-socio activity

0

0

2

2

1

0

0

100

 
 

Total

0

0

40

40

20

 

Links to the detailed Curriculum:

Credit System for S. Y. M. Tech. (Data Science) Sem-III effective from AY 2025-26

Sr. No.

Category

Course Code

Course Name

L

T

P

Hrs.

Cr

MSE/ LA1

ISE/ LA2

ESE

Remark

Professional Core (Theory)

1

PR

1DS691

 Dissertation Phase-I

0

0

24

24

12

30

30

40

POE

2

PE

Refer List

Online/NPTEL/Swayam Course

3

0

0

3

3

0

25

75

 

3

PE

Refer List

Online/NPTEL/Swayam Course

3

0

0

3

3

0

25

75

 
 
 

Total

6

0

24

30

18

 
NPTEL Couse List

Sr. No.

Course Code

Name of NPTEL courses

Link

1

1DS611

Introduction To Large Language Models (LLMs)

https://onlinecourses.nptel.ac.in/noc25_cs45/preview

2

1DS612

Deep Learning – IIT Ropar

https://onlinecourses.nptel.ac.in/noc25_cs106/preview

3

1DS613

Distributed Optimization and Machine Learning

https://onlinecourses.nptel.ac.in/noc25_cs86/preview

4

1DS614

Deep Learning for Computer Vision

https://onlinecourses.nptel.ac.in/noc25_cs93/preview

 

Credit System for S Y. M. Tech. (Data Science) Sem-IV effective from AY 2025-26

Sr. No.

Category

Course Code

Course Name

L

T

P

Hrs.

Cr

MSE/ LA1

ISE/ LA2

ESE

Remark

Professional Core (Theory)

1

PR

1DS692

Dissertation Phase-II

0

0

34

34

 17

30

30

40

POE

2

PC

1DS645

Internship

0

0

4

4

2

0

0

100

 

3

PC

1DS646

Techno-socio activity

0

0

2

2

1

0

0

100

 
 
 

Total

0

0

40

40

20

 

 

Post Graduate –Class Room/PG Seminar Hall

PG Lab

Post Graduate Lab 1

Sr. No.

Devices

Count

Specification

Installed OS

DOP

1

Dell OptiPlex 3050

3

Intel® Core™ i5-7500 CPU@ 3.40 GHz 3.41 GHz  RAM= 8GB / HDD= 1TB / SSD= 512 GB

Windows 10 Pro + Ubuntu v22

09-03-2018

2

Dell OptiPlex 3046

11

Intel® Core™ i5-6500 CPU@ 3.20 GHz 3.19 GHz  RAM= 8GB / HDD= 1TB / SSD= 512 GB

Windows 10 Pro + Ubuntu v22

25-10-2016

3

HP Prods

4

Intel® Core TM  i7-4790 CPU@ 3.60 GHz  3.60 GHz RAM= 8GB / HDD= 1TB

Windows 10 Pro + Ubuntu v22

 

4

HITACHI  CP-EX300

1

Network Projector

–

30-03-2015

Total Approximate cost =

10,06,261/- rs

Total Carpet area (sqm) =

 25.55 sqm

Total Lab Strength =

18

Post Graduate -2 / Research Lab

Sr. No.

Devices

Count

Specification

Installed OS

DOP

1

Dell OptiPlex 3046

13

Intel® Core™ i5-6500 CPU@ 3.20 GHz 3.19 GHz  RAM= 8GB / HDD= 1TB / SSD= 512 GB

Windows 10 Pro + Ubuntu v22

– 

2

Dell OptiPlex 3050

4

Intel® Core™ i5-7500 CPU@ 3.40 GHz 3.41 GHz  RAM= 8GB / HDD= 1TB / SSD= 512 GB

Windows 10 Pro + Ubuntu v22

09-03-2018

4

Samsung (Flip) WMR Screen

1

55″ Flip WMR Interactive Whiteboard

–

–

5

Epson EB-945H

1

Network Projector

–

 –

Total Approximate cost =

9,75,005/- rs

Total Carpet area (sqm) =

53.43 sqm

Total Lab Strength =

17

List of Laboratory

Sr. No.

Location

Computer Terminals

1

Post-Graduation – 1 Lab

18

2

Post-Graduation – 2/ Research Lab

17

Total

35

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