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M.Tech. CSE- Data Science & Analytics

M.Tech. CSE- Data Science & Analytics

School of Engineering and Technology (SET)

  • Programme Code

    SET0130

  • Level

    Post Graduate

  • Duration

    2 Years

About the Programme

M. Tech CSE Data Science & Analytics is a two-year post-graduate programme with an objective to impart knowledge on techniques and theories related to data science which includes statistics, data mining, data warehousing and data visualisation. The programme enables students to apply the knowledge of computing techniques and tools in the field of Big Data for solving real world problems encountered in various industries.
This programme is useful for students who wish to develop and utilise their skills to achieve a thorough knowledge of data science and engineering. Some of these are the ability to aggregate interpret and manage a large amount of heterogeneous data resources independent of the hardware and software resources. The programme consists of the modules to be learnt as compulsory electives along with core subjects of computer science and engineering. Few of them include:

  • Data Acquisition and Production
  • Machine Learning.
  • Mathematical and statistical Techniques in Computer science
  • Soft Computing Techniques
  • Analysis and Design of algorithm.
  • Image and Video Analysis
  • Massive and Graphic Analysis
  • Machine Learning
  • Advanced Web Analytics
  • Deep Learning and Web
  • Bioinformatics
  • Health Care and Analytics

Programme Educational Objectives (PEO)

Program educational objectives are broad statements that describe the career and professional accomplishments that the program is preparing graduates to achieve.
The Program Educational Objectives (PEOs) of UG Program in Computer Science & Engineering are:

  • PEO-1 The graduates will establish themselves as professionals by solving real-life problems using exploratory and analytical skills acquired in the field of Computer Science and Engineering.
  • PEO-2 The graduates will provide sustainable solutions to ever changing interdisciplinary global problems through their Research & Innovation capabilities.
  • PEO-3 The graduates will become employable, successful entrepreneur as an outcome of Industry-Academia collaboration. 
  • PEO-4 The graduates will embrace professional code of ethics while providing solution to multidisciplinary social problems in industrial, entrepreneurial and research environment to demonstrate leadership qualities.

Methods of Forming PEO’s

  • STEP 1.    The needs of the Nation and society are identified through scientific publications, industry interaction and media.
  • STEP 2.    Taking the above into consideration, the PEOs are established by the Coordination Committee of the department.
  • STEP 3.   The PEOs are communicated to the alumni and their suggestions are obtained.
  • STEP 4.   The PEOs are communicated to all the faculty members of the department and their feedback is obtained.
  • STEP 5.   The PEOs are then put to the Board of Studies of the department for final approval.

Program Outcomes (PO’s)

  • PO1:  Engineering knowledge:  Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
  • PO2:  Problem analysis:  Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
  • PO3:  Design/development of solutions:  Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
  • PO4:  Conduct investigations of complex problems:  Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
  • PO5:  Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
  • PO6:  The engineer and society:  Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
  • PO7:  Environment and sustainability:  Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
  • PO8:  Ethics:  Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
  • PO9:  Individual and team work:  Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  • PO10:  Communication:  Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
  • PO11:  Project management and finance:  Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
  • PO12:  Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

PSO's:

  • PSO1:  Experiment and prepare programming concepts and provide new ideas and innovations towards research and societal issues.
  • PSO2:  Analyse and develop computer programs in the areas related to algorithms, system software, cloud computing, artificial intelligence & machine learning, bioinformatics, big data analytics, block chain, cyber security and networking for efficient design of computer-based systems of varying complexity.
  • PSO3:  Apply standard Software Engineering practices and strategies in software project development using open-source programming environment to deliver a quality product for business success.

This course is for individuals who...

are interested in developing computational and statistical skills for data-driven multi-disciplinary problems.

Students who are looking for...

a career in areas such as machine learning, deep learning, data mining, predictive analysis, large scale data analytics and big data.

Course Fee
For National Students
1st Year 97850 2nd Year 100786
For International Students
Fee Per Semester Fee Per Year
2550 5000**
Programme Structure

School of Engineering and Technology

Department Of Computer Science & Engineering

M.Tech CSE with specialization in Software Engineering

Batch: 2019 Onwards

 

TERM: I (Spring-II)

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

THEORY SUBJECTS

1

CSE611

Analysis and Design of Algorithms

3

1

0

4

2

CSE613

Mathematical and Statistical Techniques in Computer Science

3

1

0

4

3

 

Departmental Elective-1

3

0

0

3

CSE640

Object Oriented Software Engineering

CSE651

Software Architecture and Design Pattern.

4

 

Departmental Elective-2

 

 

 

 

CSE642

Soft Computing Techniques

3

0

0

3

5

 

Departmental Elective-3

3

0

0

3

CSE643

Software Requirement and Estimation

CSE652

Software Quality Metrics and Testing

Practical/Viva-Voce/Jury

1

CSP611

Analysis and Design of Algorithms Lab

0

0

2

1

2

 

Departmental Elective-1

0

0

2

1

CSP640

Object Oriented Software Engineering Lab

CSP651

Software Architecture and Design Pattern Lab

TOTAL CREDITS

19

School of Engineering and Technology

Department Of Computer Science & Engineering

M.Tech CSE with specialization in Data Science & Analytics

Batch: 2019 Onwards

 

TERM: I (Spring-II)

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

THEORY SUBJECTS

1

CSE611

Analysis and Design of Algorithms

3

1

0

4

2

CSE613

Mathematical and Statistical Techniques in Computer Science

3

1

0

4

3

 

Departmental Elective-1

3

0

0

3

CSE604

Data Acquisition and Production

 

Massive Graph Analysis

4

 

Departmental Elective-2

 

 

 

 

CSE642

Soft Computing Techniques

3

0

0

3

5

 

Departmental Elective-3

3

0

0

3

CSE622

Advanced Data Mining Techniques

 

Image and Video Analysis

Practical/Viva-Voce/Jury

1

CSP611

Analysis and Design of Algorithms Lab

0

0

2

1

2

 

Departmental Elective-1

0

0

2

1

CSP604

Data Acquisition and Production

 

Massive Graph Analysis

TOTAL CREDITS

19

School of Engineering and Technology

Department Of Computer Science & Engineering

M.Tech CSE with specialization in Networking and Cyber Security

Batch: 2019 Onwards

 

TERM: I (Spring-II)

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

THEORY SUBJECTS

1

CSE611

Analysis and Design of Algorithms

3

1

0

4

2

CSE613

Mathematical and Statistical Techniques in Computer Science

3

1

0

4

3

 

Departmental Elective-1

3

0

0

3

CSE630

Advanced Computer Network

 

Vehicular Communication Network

4

 

Departmental Elective-2

 

 

 

 

CSE642

Soft Computing Techniques

3

0

0

3

5

 

Departmental Elective-3

3

0

0

3

  CSE634

Advanced Mobile computing

CSE632

Advanced Network Security

Practical/Viva-Voce/Jury

1

CSP611

Analysis and Design of Algorithms Lab

0

0

2

1

2

 

Departmental Elective-1

0

0

2

1

CSP630

Advanced Computer Network

 

Vehicular Communication Network

TOTAL CREDITS

19

School of Engineering and Technology

Department Of Computer Science & Engineering

M.Tech CSE with specialization in Software Engineering

Batch: 2019 Onwards

 

TERM: II (Spring-I)

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

THEORY SUBJECTS

1

CSE650

Pattern Recognition

3

1

0

4

2

CSE605

Machine Learning

3

0

0

3

3

 

Departmental Elective-4

3

0

0

3

CSE644

Agile Based Software Engineering

  CSE649

Secure Software Engineering

4

 

Departmental Elective-5

2

0

0

2

  CSE648

Recent Advances in Software Engineering.

5

 

Departmental Elective-6

3

0

0

3

CSE635

Software Reliability Engineering

 

Web Engineering

6

 

Departmental Elective-7

3

0

0

3

CSE647 

Component Based Software Engineering 

7

MRM001

Research Methodology

2

0

0

2

Practical/Viva-Voce/Jury

1

CSP650

Pattern Recognition Lab

0

0

2

1

2

 

Departmental Elective-4

0

0

2

1

CSP644

Agile Based Software Engineering Lab

  CSP649

Secure Software Engineering Lab

3

 

Departmental Elective-5

0

0

2

1

  CSP648

Recent Advances in Software Engineering Lab

4

CCU101

Community Connect

-

-

-

2

TOTAL CREDITS

25

School of Engineering and Technology

Department Of Computer Science & Engineering

M.Tech CSE with specialization in Data Science & Analytics

Batch: 2019 Onwards

 

TERM: II (Spring-I)

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

THEORY SUBJECTS

1

CSE650

Pattern Recognition

3

1

0

4

2

CSE605

Machine Learning

3

0

0

3

3

 

Departmental Elective-4

3

0

0

3

 

Bioinformatics

CSE618

Big Data Analytics

4

 

Departmental Elective-5

2

0

0

2

  CSE610

Advance Web Analytics

 

Internet of Things and its applications.

5

 

Departmental Elective-6

3

0

0

3

CSE620

Deep Learning and web

 

Health Care and Analytics

6

 

Departmental Elective-7

3

0

0

3

CSE608

Natural Language Computing

7

MRM001

Research Methodology

2

0

0

2

Practical/Viva-Voce/Jury

1

CSP601

Pattern Recognition

0

0

2

1

2

 

Departmental Elective-4

0

0

2

1

 

Bioinformatics

  CSP618

Big Data Analytics

3

 

Departmental Elective-5

0

0

2

1

  CSP610

Advance Web Analytics

 

Internet of Things and its applications.

4

CCU101

Community Connect

-

-

-

2

TOTAL CREDITS

25

School of Engineering and Technology

Department Of Computer Science & Engineering

M.Tech CSE with specialization in Networking and Cyber Security

Batch: 2019 Onwards

 

TERM: II (Spring-I)

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

THEORY SUBJECTS

1

CSE650

Pattern Recognition

3

1

0

4

2

CSE605

Machine Learning

3

0

0

3

3

 

Departmental Elective-4

3

0

0

3

  CSE646

Wireless Sensor Network

CSE616

Intrusion Detection & Prevention 

CSE606

Cloud Services in Mobile

 

Applications Programming

4

 

Departmental Elective-5

2

0

2

3

  CSE629

Performance Modeling of Computer Communication network

  CSE607

Grid Computing

3

0

0

5

 

Departmental Elective-6

3

0

0

3

  CSE628

Ad Hoc Wireless Networks

  CSE633

Advanced Wireless Communication

6

 

Departmental Elective-7

3

0

0

3

  CSE641

Malware Analysis, Detection & Prevention

  CSE617

Advanced Cryptography

7

MRM001

Research Methodology

2

0

0

2

Practical/Viva-Voce/Jury

1

CSP650

Pattern Recognition

0

0

2

1

2

 

Departmental Elective-4

0

0

2

1

  CSP646

Wireless Sensor Network

CSP616

Intrusion Detection & Prevention 

CSP606

Cloud Services in Mobile

 

Applications Programming

3

 

Departmental Elective-5

0

0

2

 

CSP629

Performance Modeling of Computer Communication network

4

CCU101

Community Connect

-

-

-

2

TOTAL CREDITS

25

School of Engineering and Technology

Department Of Computer Science & Engineering

Master of Technology- Computer Science and Engineering

Batch: 2019 Onwards

 

TERM: III

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

Practical/Viva-Voce/Jury

1

CSP681

Seminar

-

-

-

2

2

CSP691

Dissertation 1

-

-

-

10

TOTAL CREDITS

 

 

 

 

12

School of Engineering and Technology

Department Of Computer Science & Engineering

Master of Technology- Computer Science and Engineering

Batch: 2019 Onwards

 

TERM: IV

S. No.

Course Code

Course

Teaching Load

Credits

L

T

P

Practical/Viva-Voce/Jury

1.           

CSP692

Dissertation-II

-

-

-

                                                                                                          16

TOTAL CREDITS

 

 

 

 

16

Eligibility Criteria
For National Students
  • B.Tech. in Computer Science IT Electronics & Communication Electronics & Instrumentation and Electrical & Electronics with minimum 60% marks. MCA/M.Sc. (Computer Sc.) with minimum 60% marks.
  • GATE/NET Qualified students shall be preferred.
For International Students The eligibility criterion for all programs for international applicants is minimum 50% in the qualifying examination and having studied the pre-requisite subjects for admission in to the desired program.
Career path you can choose after the course
  • Data Scientist
  • Data Analytic
  • R& D
  • Banks
  • IT Consultancies
  • Research
  • Academician in Colleges/Universities

Students of this programme are placed in companies with an average annual package of 4.50 LPA to 12.00 LPA

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