MSc Data Science

Overview

Data Science is a data-driven discipline that brings together computer science with a focus on machine learning and algorithms, mathematics, statistics, and business or domain knowledge. It is all about extracting meaningful insights from large volumes of data using modern tools and techniques, identifying hidden patterns, and enabling informed business decisions.

The MSc Data Science program is designed for aspiring data scientists to develop core skills in machine learning, deep learning, data visualization, and cloud computing.

What You’ll Learn

Artificial Intelligence and Machine Learning
Cloud Computing
Big Data technologies (Hadoop, Spark)
NoSQL databases
Deep learning and NLP
Blockchain technology
Internet of Things(IoT)
Data Structures
Algorithms to support advanced data analysis and modeling.

Practical Skills

With a strong emphasis on application-oriented learning and industry-relevant coursework, graduates of the MSc Data Science program develop strong

Analytical skills
Programming skills
Data-driven problem-solving skills
They become capable of acquiring, managing, and analyzing large-scale data, building and deploying machine learning models, developing data-driven applications, and applying statistical and computational techniques to real-world problems.

Career Pathways

Programme Education Objectives

PEOs	Programme Educational Objectives
PEO1	To develop in-depth understanding of the key technologies in Data Science.
PEO2	To gain ability to design integrated solutions to problems by applying principles of Data Science.
PEO3	To provide strong core training both theoretically and practically to create adept data scientists.
PEO4	To gain exposure to real-world problems in order to adapt easily to the industry demands.
PEO5	To inculcate values and make students realize the significance of ethics in a professional environment.

Programme Outcomes (POs)

POs	Programme Outcomes
PO1	Apply Quantitative modeling and Data analysis techniques to solve real-world problems, communicate findings, and effectively present results.
PO2	Apply Data-driven insights to enhance business and develop applications that would reduce human effort.
PO3	Employ cutting edge tools and technologies to analyze data particularly, large volume of data.
PO4	Apply appropriate algorithms and build intelligent machines by following ethical practices and adapt to industry demands.
PO5	Pursue quality research in Data Science by following ethical and moral values and develop solutions to societal needs.

Programme Structure

Semester I	Semester II	Semester III	Semester IV
Probability and Distribution Models	Big Data and No SQL	Advanced Deep Learning	a. Blockchain Technology b. IoT
Python Programming	Advanced Algorithms	Natural Language Processing	Project / Internship
Artificial Intelligence and Machine Learning	Deep Learning	Cloud Computing	-
Exploratory Data Analysis	Optimization Techniques	a. Full Stack Web Development b. Data Security and Privacy	-
Data Visualization Lab	Research Methodology	Advanced Deep Learning Lab	-
Data Analysis using Python Lab	Big Data and No SQL Lab	a. Full Stack Web Development Lab b. Data Security and Privacy Lab	-
Artificial Intelligence and Machine Learning Lab	Deep Learning Lab	Mini Project	-

Semester I

Course Code	Name of Course	Teaching Hours	Credits
25MDS 101	Probability and Distribution Models	52	4
25MDS 102	Python Programming	52	4
25MDS 103	Artificial Intelligence and Machine Learning	52	4
25MDS 104	Exploratory Data Analysis	52	4
25MDS SC1	Data Visualization Lab	52	2
25MDS 105	Data Analysis using Python Lab	96	4
25MDS 106	Artificial Intelligence and Machine Learning Lab	96	4
Total Credits			26

Semester II

Course Code	Name of Course	Teaching Hours	Credits
25MDS 201	Big Data and No SQL	52	4
25MDS 202	Advanced Algorithms	52	4
25MDS 203	Deep Learning	52	4
25MDS 204	Optimization Techniques	52	4
25MDS SC2	Research Methodology	30	2
25MDS 205	Big Data and No SQL Lab	96	4
25MDS 206	Deep Learning Lab	96	4
Total Credits			26

Semester III

Course Code	Name of Course	Teaching Hours	Credits
25MDS 301	Advanced Deep Learning	52	4
25MDS 302	Natural Language Processing	52	4
25MDS 303	Cloud Computing	52	4
25MDS 304X	a. Full Stack Web Development b. Data Security and Privacy	52	4
25MDS 305	Advanced Deep Learning Lab	96	4
25MDS 306X	a. Full Stack Web Development Lab b. Data Security and Privacy Lab	96	4
25MDS 307	Mini Project	96	4
Total Credits			28

Semester IV

Course Code	Name of Course	Teaching Hours	Credits
25MDS 401X	c. Blockchain Technology d. IoT	52	4
25MDS 402	Project / Internship	26	16
Total Credits			20

Note: Students must complete a certification programme on a MOOC platform such as SWAYAM, NPTEL, or AICTE of at least 30 hours, starting from the first semester, and submit the certificate before the third semester examinations.

Value Added Courses (VACs)

S.No	Name of the VAC
1	RAG – Retrieval Augmented Generation
2	Agentic AI
3	Hands on cloud computing on AWS
4	Cyber Security
5	Mobile Application Development
6	Ethical hacking
7	Web Application development- Front end
8	Web Application development- Back end

Eligibility Criteria

Candidates must have completed a Bachelor of Computer Applications (BCA) with a minimum of 50 percent aggregate marks from any UGC-recognized university in India or abroad.

Candidates with an undergraduate degree in Science with at least 50 percent aggregate from any UGC-recognized university in India or abroad are also eligible, provided they have studied any two of the following subjects as major or minor: