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M.Sc. Data Science & Analytics

M.Sc. Data Science & Analytics

Sharda School of Engineering & Science (SSES)

  • Programme Code

    SBSR0309

  • Level

    Post Graduate

  • Duration

    2 Years

About the Programme

Data science is poised to reshape our global economy, reinventing how we conduct business, and improving our lives in a variety of ways. Data scientists are in high demand across industries, with their ability to assist businesses in making data-driven decisions being highly valued.

You've come to the correct place if you're interested in extracting knowledge and insights from large data sets and want to put your skills to use in a meaningful profession. The Sharda University curriculum will assist you in learning the skills required for a successful data science profession.

To mention a few, you'll learn essential competencies in Machine Learning, Data Mining, and Predictive Analytics.

*M.Sc. (CW) - M.Sc. with Course Work

*M.Sc. (RW) - M.Sc. with Research Work

*M.Sc. (CW + RW) - M.Sc. with Course Work & Research Work

Programme Educational Objectives (PEO’s)

  • PEO1: The graduates will achieve deep subject knowledge in the courses of study to enable employed in industry, government and entrepreneurial endeavors to have a successful professional career.
  • PEO2: The graduates will develop positive attitude and skills to enable a multi facet personality.
  • PEO3: The graduates will prepare for pursue higher education and research.
  • PEO4: The graduates will develop for contribute to the society and human well-being by applying ethical principles.

Program Outcomes (PO’s)

  • PO1: Data Science knowledge: Engage in continuous reflective learning in the context of technology and scientific advancement.
  • PO2: Modern software tool usage: Acquire the skills in handling data science programming tools towards problem solving and solution analysis for domain specific problems.
  • PO3: Critical thinking: Ability to understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science.
  • PO4: Problem analysis: Problem analysis and design ability to identify analyze and design solutions for data science problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines.
  • PO5: Innovation and Entrepreneurship:Produce innovative IT solutions and services based on global needs and trends.

Programme Specific Outcomes (PSO’s)

  • PSO1: Utilize the data science theories for societal and environmental concerns.
  • PSO2: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices.
  • PSO3: 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.
  • PSO4: Understand the role of statistical approaches and apply the same to solve the real life problems in the fields of data science and apply the research-based knowledge to analyse and solve advanced problems in data science

This course is for individuals who...

This programme is for students who wish to learn about advanced data analytic methods that are currently available on the market. The training focuses on the development of skills and an understanding of how to properly use available data to gain superior insights. The course will concentrate on the models, tools, and procedures for analyzing data from a range of sources.

Students who are looking for...

A profession in a field that is always evolving, with an almost limitless range of data generation, gathering, processing, and analysis. Financial stability, the ability to relocate, and the opportunity to learn for the rest of one's life are just a few of the benefits that make this programme an excellent choice for computer science enthusiasts.

Successful graduates of the course will have access to a variety of career opportunities. The course's successful graduates are offered a preliminary average compensation of INR 3 to 8 Lacs, which is based on the candidate's experience in the field.

After completing the course, students might find work in both the commercial and public sectors.

Course Fee
For National Students
1st Year 130000 2nd Year 133900
For International Students
Fee Per Semester Fee Per Year
NA 5000*
Programme Structure

S. No.

SUBJECT

CODE

Title of Paper

Teaching Load

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

  1. CC
  2. AECC
  3. SEC
  4. DSE

 

THEORY

 

 

 

 

 

 

 

 

L

T

P

TOTAL

 

 

 

 

 

 

 

 

 

 

 

 

 

1.

MDA101

Foundations of Data Science

4

0

0

4

4

CO-REQUISITE

CC

2.

MDA102

Mathematics for Machine Learning

4

0

0

4

4

CO-REQUISITE

CC

3.

STT4701

Distributions Theory

4

0

0

4

4

CO-REQUISITE

CC

4.

STT4704

Probability & Statistical Methods

4

0

0

4

4

CO-REQUISITE

CC

 

PRACTICALS

 

 

 

 

 

 

 

 

5.

DAP4754

Data Science Lab

0

0

2

2

 

1

CO-REQUISITE

CC

6.

DAP4755

Mathematics for Machine Learning Lab

0

0

2

2

1

CO-REQUISITE

CC

7.

  STP4753

Distributions Theory Lab

0

0

2

2

1

CO-REQUISITE

CC

8.

STP4752

Statistical Methods Lab

0

0

2

2

1

CO-REQUISITE

CC

9.

CCP4001

Community Connect

-

-

4

2

0

CO-REQUISITE

          SEC

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

Teaching Load

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

  1. CC
  2. AECC
  3. SEC
  4. DSE

 

THEORY

 

 

 

 

 

 

 

 

L

T

P

TOTAL

 

 

 

1.

MDA105

Regression Analysis and Predictive Models

4

0

0

4

4

 

DSE

2.

MDA107

Advanced Big Data and Text Analytics

4

0

0

4

4

 

DSE

3.

STT4803

Time Series Analysis & Vital Statistics

3

0

0

3

3

 

DSE (OE)

4.

MDA108

Data Mining & Artificial Intelligence

4

0

0

4

4

 

SEC

 

PRACTICALS

 

 

 

 

 

 

 

 

5.

    STP4854

Time Series Analysis Lab

0

0

2

2

1

 

DSE (OE)

6.

DAR4856

Project

0

0

8

8

4

 

            Project

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

Teaching Load

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

1. CC

2. AECC

3. SEC

4. DSE

 

THEORY

 

L

T

P

TOTAL

 

 

 

1.

  MDA201

Inferential Statistics

4

0

0

4

4

 

CC

2.

MDA202

Multivariate Data Analysis

4

0

0

4

4

 

CC

3.

MDA215

Advances in Design of Experiment

4

0

0

4

4

 

DSE

4.

    STT5305

RM & IPR

1

0

0

1

1

 

SEC

 

PRACTICAL

 

 

 

 

 

 

 

 

5.

STP5351

Inference Lab

0

0

2

2

1

 

CC

6.

STP5352

Multivariate Analysis Lab

0

0

2

2

1

 

CC

7.

STP5454

Design of Experiments Lab

0

0

2

2

1

 

DSE

8.

DAP5358

Exploratory Data Analysis with Tableau & Power BI

0

0

4

4

2

 

SEC

 

DISSERTATION

 

 

 

 

 

 

 

 

9.

STR5356

DISSERTATION-I                           

0

0

4

4

2

 

Project

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

HOURS

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

1. CC

2. AECC

3. SEC

4. DSE

 

THEORY

 

 

 

 

 

 

 

 

L

T

P

TOTAL

 

 

 

1.

STT5403

Reliability and Survival Analysis

5

0

0

5

5

 

CC

2.

STT5401

Statistical Quality Control

4

0

0

4

4

 

CC

3.

DAT5404

Deep Learning and Neural Network

5

0

0

5

5

 

DSE

 

PRACTICAL

 

 

 

 

 

 

 

 

4.

STP5454

Reliability and Survival Lab

0

0

2

2

1

 

                CC

5.

STP5455

Quality Control Lab

0

0

2

1

1

CO-REQUISITE

 

CC

6.

DAP5459

Deep Learning Lab

0

0

2

1

1

 

DSE

 

 

 

 

 

 

 

 

 

 

 

DISSERTATION

 

 

 

 

 

 

 

 

7.

STR5457

DISSERTATION-2   

0

0

6

6

3

CO-REQUISITE

 

RBL-2

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

Teaching Load

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

  1. CC
  2. AECC
  3. SEC
  4. DSE

 

THEORY

 

L

T

P

TOTAL

 

 

 

1.

MDA201

Inferential Statistics

4

0

0

4

4

 

CC

2.

MDA202

Multivariate Data Analysis

4

0

0

4

4

 

CC

3.

DAT5303

Introduction of Deep Learning

3

0

0

3

3

 

DSE

4.

STT5305

RM & IPR

1

0

0

1

1

CO-REQUISITE

 

SEC

 

  Practical

 

 

 

 

 

 

 

 

5.

STP5351

Inference Lab

0

0

2

2

1

 

CC

6.

STP5352

Multivariate Analysis Lab

0

0

2

2

1

 

CC

7.

DAR5356

Capstone Project I

0

0

12

12

6

 

Project

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

HOURS

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

  1. CC
  2. AECC
  3. SEC
  4. DSE

 

THEORY

 

 

 

 

 

 

 

 

L

T

P

TOTAL

 

 

 

1.

STT5403

Reliability and Survival Analysis

5

0

0

5

5

 

CC

 

PRACTICAL

 

 

 

 

 

 

 

 

2.

STP5454

Reliability and Survival Lab

0

0

2

2

1

 

                CC

 

DISSERTATION

 

 

 

 

 

 

 

 

3.

DAR5457

Capstone project II

0

0

 

28

28

14

 

        Project

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

Teaching Load

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

  1. CC
  2. AECC
  3. SEC
  4. DSE

 

THEORY

 

L

T

P

TOTAL

 

 

 

1.

DAT5303

Introduction of Deep Learning

3

0

0

3

3

CO-REQUISITE

DSE

2.

STT5305

RM & IPR

1

0

0

1

1

 

SEC

 

DISSERTATION

 

 

 

 

 

 

 

 

3.

DAR5360

DISSERTATION-I                             

0

0

32

32

16

 

Project

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

S. No.

SUBJECT

CODE

Title of Paper

Teaching Load

CREDITS

PRE-REQUISITE/CO-REQUISITE

 

Type of Course[1]:

  1. CC
  2. AECC
  3. SEC
  4. DSE

 

THEORY

 

L

T

P

TOTAL

 

 

 

1.

MDA502

Multivariate data Analysis

4

0

0

4

4

CO-REQUISITE

DSE

 

DISSERTATION

 

 

 

 

 

 

 

 

2.

DAR5461

DISSERTATION-II                           

0

0

32

32

16

 

Project

TOTAL

 

 

 

 

20

 

 


[1] CC: Core Course, AECC: Ability Enhancement Compulsory Courses, SEC: Skill Enhancement Courses, DSE: Discipline Specific Courses

Eligibility Criteria
For National Students
  • B.Sc. IT/CS/Mathematics, BCA with minimum 50% marks
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.
How to apply for the course?

 

Career path you can choose after the course
  • Research Scientist
  • Data Analyst
  • Data Scientist/Sr. Data Scientist
  • Business Intelligence Developer

 

Take the next step towards a career in basic sciences.

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