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Bachelor of Science- Data Science & Analytics

Bachelor of Science- Data Science & Analytics

Sharda School of Engineering & Science (SSES)

  • Programme Code

    SBR0308

  • Level

    Graduate

  • Duration

    3 Years

About the Programme

The B.Sc. in Data Science is an undergraduate programme designed to equip students with the skills and knowledge necessary to extract meaningful insights from data. It blends core subjects such as mathematics, statistics, and computer science with specialized courses in data analysis, machine learning, programming, and data visualization. Students learn to handle large datasets, build predictive models, and apply analytical techniques to real-world problems across various domains like finance, healthcare, and technology. The programme emphasizes both theoretical foundations and practical applications, often including projects, internships, and exposure to industry-standard tools and technologies to prepare graduates for careers in data-driven roles or for further studies in related fields.

Programme Educational Objectives (PEO’s)

  • PEO1: Prepare professionals conversant with current and advanced technological tools to carry out Investigation, analysis and synthesis by identifying various compute-oriented solutions.
  • PEO2: To develop positive attitude and skills which enable them to become a multi facet personality.
  • PEO3: To prepare students in such a way so that they perform excellently in national lavel entrance examinations conducted by various well-known institution like IIT’s/ central Universities/other academic institutes etc. to pursue their PG/MS/Dual PG and Ph.D. programs.
  • PEO4: To make them aware of effective machine learning and Artificial Intelligence based data analytics and inference required for Industrial Application.
  • PEO5: To inculcate passion for lifelong learning by introducing principles of group dynamics, public policies, environmental and societal context.

Program Outcomes (PO’s)

  • PO1. Complex Problem Solving: Solve different kinds of problems in familiar and non-familiar contexts and apply the learning to real-life situations.
  • PO2. Critical Thinking: Analyze and synthesize data from a variety of sources and draw valid conclusions and support them with evidence and examples.
  • PO3. Creativity: Demonstrate the ability to think ‘out of the box’ and generate solutions to complex problems in unfamiliar contexts by applying concepts of multidisciplinary and interdisciplinary.
  • PO4. Analytical reasoning/thinking: Evaluate the reliability and relevance of evidence.
  • PO5. Research-related skills: Demonstrate the ability to acquire the understanding of basic research ethics and skills in practicing/doing ethics in the field/ in personal research work, regardless of the funding authority or field of study.
  • PO6. Communication Skills: Demonstrate the skills that enable them to express thoughts and ideas effectively in writing and orally and communicate with others using appropriate media.
  • PO7. Coordinating/collaborating with others: Demonstrate the ability to work effectively and respectfully with diverse teams using management skills to guide people to the right destination.
  • PO8. Digital and technological skills: Demonstrate the capability to access, evaluate, and use a variety of relevant information sources, and use appropriate software for analysis of data.
  • PO9. Value Inculcation: Instill integrity and identify ethical issues related to work, and follow ethical practices with or understand the perspective, experiences, or points of view of another individual or group, and to identify and understand other people’s emotions.
  • PO10. Sustainability Growth: Demonstrate the capability to lead a diverse team or individual to accomplish and participate in community-engaged services/ activities for promoting the well-being of society to mitigating the effects of environmental degradation, climate change, and pollution.
  • PO11. Multidisciplinary Life-long learning: Comprehensive knowledge and coherent understanding of the chosen disciplinary/interdisciplinary areas of study in a broad multidisciplinary context by inculcating a healthy attitude to be a lifelong learner.

This course is for individuals who...

are interested in harnessing data to derive meaningful insights, solve real-world problems, and support decision-making using statistical, computational, and analytical tools.

Students who are looking for...

a career at the intersection of technology, statistics, and business—where they can work with data to uncover insights, develop predictive models, and contribute to data-driven decision-making across various industries.

Course Fee
For National Students
1st Year 120000 2nd Year 123600 3rd Year 127308
For International Students
Fee Per Semester Fee Per Year
NA 3400*
Programme Structure

S. No.

Course Code

Course Name

Teaching Load

Credits

Pre-Requisite/ Co-Requisite

Type of Course:

1. CC   2. DSE

3. OPE 4. SEC

5. AEC 6. VAC

  7.Project

 

THEORY

 

L

T

P

TOTAL

 

 

 

1.

MSM101

Foundation Course in Mathematics

4

0

0

4

4

Basic Mathematics upto 10+2

CC Major

2.

DAT1101

Foundation of Data Science

3

0

0

3

3

 

CC Major

3.

CMS102

Descriptive Statistics

3

0

0

3

3

Basic Mathematics upto 10+2

(Minor)

4.

MTT1101

Programming for Problem Solving

2

0

0

2

2

 

Multi Dis (DSE)

5.

EVT1129

  Environmental Education

2

0

0

2

2

 

VAC

 

PRACTICALS

 

 

 

 

 

 

 

 

6.

DAP1151

Foundation of Data Science Lab

 

0

0

2

2

1

 

CC Major

7.

SKP1010

Communicative Essentials-1

1

0

2

3

2

 

AEC

8.

VOM103

Essential Excel Skills for Business

0

0

6

6

3

 

SEC

TOTAL CREDITS

 

 

 

 

20

 

 

S. No.

Course Code

Course Name

Teaching Load

Credits

Pre-Requisite/ Co-Requisite

Type of Course:

1. CC     2. DSE

3. OPE 4. SEC

5. AEC 6. VAC

  7.Project

 

THEORY

 

L

T

P

TOTAL

(hrs)

 

 

 

1.

CMS131

Matrix Analysis and Linear Algebra

4

0

0

4

4

Pre-requisite MSM101

CC

2.

MTT1202

Principal of Data Structures

3

0

0

3

3

 

CC

3.

CMS132

Mathematical Expectations & Probability Distributions

3

0

0

3

3

 

  Minor

4.

VAC110

Yoga for Holistic Health

2

0

0

2

2

 

VAC

5.

IKT1104

Mulya Pravah

2

0

0

2

2

 

VAC

 

PRACTICALS

 

 

 

 

 

 

 

 

6.

MTP1251

Principles of Data Structures Lab

0

0

2

2

1

 

CC

7.

ARP102

Communicative English-2

1

0

2

3

2

 

AEC

8.

VOM104

Advanced Excel Skills for Business

0

0

6

6

3

Pre-requisite VOM103

SEC

TOTAL CREDITS

 

 

 

 

20

 

 

S. No.

Course Code

Course Name

Teaching Load

Credits

Pre-Requisite/ Co-Requisite

Type of Course:

1. CC     2. DSE

3. OPE 4. SEC

5. AEC 6. VAC

  7.Project

 

THEORY

 

L

T

P

TOTAL

(hrs)

 

 

 

1.

BDA217

Data Preparation and Data Cleaning

3

0

0

3

3

 

CC Major

2.

BDA313

Regression, Time Series, Forecasting and Index Numbers

5

0

0

5

5

 

CC Major

3.

BDA215

Operation Research

3

0

0

3

3

 

  Minor

4.

IKT2202

General Hindi (Indian Language)

2

0

0

2

2

 

AEC

 

PRACTICALS

 

 

 

 

 

 

 

 

5.

DAP2351

Data Preparation and Data Cleaning lab

0

0

4

4

2

 

CC Major

6.

AI3407

Prompt Engineering for AI and Data Science

0

0

4

4

2

 

DSE(Multi/Inter-discipli)

7.

VOM2305

Data Visualization with Tableau and Power BI

0

0

6

6

3

 

SEC

8.

DAR2351

Research Based Learning-I(RBL-1)

 

0

0

2

2

0

 

Research Project

TOTAL CREDITS

 

 

 

 

20

 

 

S. No.

Course Code

Course Name

Teaching Load

Credits

Pre-Requisite/ Co-Requisite

Type of Course:

1. CC     2. DSE

3. OPE 4. SEC

5. AEC 6. VAC

  7.Project

 

THEORY

 

L

T

P

TOTAL

(hrs)

 

 

 

1.

BDA202

Database Management Systems

4

0

0

4

4

 

CC

2.

BDA214

Sampling Theory

4

0

0

4

4

 

CC

3.

BDA323

Multivariate Data Analysis

4

0

0

4

4

 

CC

 

Practicals

 

 

 

 

 

 

 

 

4.

DAP2452

Sampling Lab

0

0

4

4

2

 

CC

5.

AI3408

Supervised & Unsupervised Learning Techniques

0

0

6

6

3

 

Minor

6.

CCU108

Community Connect

0

0

2

2

2

 

AEC

7.

DAR2452

Research Based Learning-2(RBL-2)

0

0

2

2

1

 

Project

TOTAL CREDITS

 

 

 

 

20

 

 

S. No.

Course Code

Course Name

Teaching Load

Credits

Pre-Requisite/ Co-Requisite

Type of Course:

1. CC     2. DSE

3. OPE 4. SEC

5. AEC 6. VAC

  7.Project

 

THEORY

 

L

T

P

TOTAL

(hrs)

 

 

 

1.

BDA303

Machine Learning

4

0

0

4

4

 

CC

2.

BDA322

Statistical Simulation

4

0

0

4

4

 

CC

3.

BDA318

Data Visualization

4

0

0

4

4

 

CC

4.

BDA216

Statistical Inference

4

0

0

4

4

 

CC

 

Practicals

 

 

 

 

 

 

 

 

5.

DAP3551

Machine Learning Lab

0

0

4

4

2

Co-requisite BDA303

CC

6.

DAP3552

Statistical Simulation Lab

0

0

4

4

2

Co-requisite BDA322

CC

7.

DAR3551

Research Based Learning-III

(RBL-3)

0

0

0

0

0

Pre-requisite RBL002

Project

TOTAL CREDITS

 

 

 

 

20

 

 

S. No.

Course Code

Course Name

Teaching Load

Credits

Pre-Requisite/ Co-Requisite

Type of Course:

1. CC     2. DSE

3. OPE 4. SEC

5. AEC 6. VAC

  7.Project

 

THEORY

 

L

T

P

TOTAL

(hrs)

 

 

 

1.

BDA321

    Non-Parametric Statistical Inference

    4

0

0

4

4

 

  Minor

2.

BDA218

Data Ware Housing and Data Mining

3

0

0

3

3

 

Minor

3.

BDA325

Deep Learning

3

0

0

3

3

 

DSE

 

Practicals

 

 

 

 

 

 

 

 

4.

BDA270

Data Ware Housing and Data Mining Lab

0

0

2

2

1

Co-requiste MSM312

Minor

5

AI3409

Advanced Machine Learning Techniques

0

0

8

8

4

 

Minor

6.

ARP306

Campus to Corporate

1

0

2

3

2

AEC

AEC

7.

INC001

Industry Connect

0

0

4

4

2

 

Project

8.

DAR3652

  Research Based Learning-IV

(RBL-4)

0

0

2

2

1

Pre-requisite

RBL003

Project

TOTAL CREDITS

 

 

 

 

20

 

 

Eligibility Criteria
For National Students
  • Sr. secondary (10+2) with minimum 55% marks in PCM/PCB/Humanities with Maths or Applied Maths/Commerce with Maths or Applied Maths
  • Proficiency in English communication
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 Analyst 
  • Data Scientist 
  • Business Analyst 
  • Machine Learning Engineer 
  • Data Engineer
  • AI/ML Research Assistant 
  • Statistician 
  • Quantitative Analyst 
  • Data Consultant 
  • Data Visualization Specialist

 

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