M.Sc Data Science Syllabus and Subjects


2 Years

Average Fees

INR 1.7 L - 3.5 LPA

Rahil Ahmed

Updated on Jan 4, 2023 by Rahil Ahmed

Rahil Ahmed

Updated on Jan 4, 2023 by Rahil Ahmed

The Masters in Data Science syllabus deals with the major disciplines, techniques, and theories of Calculus, Descriptive Statistics, and C-Programming in order to understand the various phenomena with respect to a big set of real-world data. Some common M.Sc Data Science subjects are statistics, mathematics, coding, machine learning, etc. 

Semester Wise M.Sc Data Science Syllabus 

M.Sc Data Science is a postgraduate graduate course of two years with four semesters for the students to go through. It is designed with the goal of forming capable and analytical scientists and researchers who are able to solve the most complex of data to help advance the technological limits of the world.

It makes the student aware of the situations and trials they have to get accustomed to in order to survive in this hard and competitive world while still chasing after something that makes one reach their goal. The M.Sc Data Science course is designed to help students gain a deep insight into the field and subject matter.

The curriculum consists of core courses devoted to analysis and understanding the complexity of the data world. The elective courses are oriented closer to comprehensive data analysis for the students to understand. The semester wise syllabus for M.Sc Data Science is as given below:


M.Sc Data Science First Year Syllabus

Semester I

Semester II

Mathematical Foundation For Data Science

Mathematical Foundation For Data Science – II

Probability And Distribution Theory

Regression Analysis

Principles of Data Science

Design and Analysis of Algorithms

Fundamentals of Data Science

Machine learning

Python Programming

Advanced Python Programming for Spatial Analytics

Introduction to Geospatial Technology

Image Analytics


M.Sc Data Science Second Year Syllabus

Semester III

Semester IV

Spatial Modeling

Industry Project

Summer Project

Research Work


Research Publication

Natural Language Processing

Exploratory Data Analysis

M.Sc in Data Science Subjects

The semester-wise M.Sc Data Science subjects aim to impart knowledge and deep understanding of the concepts to the students. The M.Sc Data Science course syllabus includes both theoretical classroom-based teaching and practical visit sessions for a better understanding of advanced application-related topics. The curriculum consists of both core and elective subjects to make the two-year-length course more flexible. M.Sc Data Science subjects list is as follows:

  • Statistics
  • Coding
  • Business Intelligence
  • Data Structures
  • Mathematics
  • Machine Learning
  • Algorithms

M.Sc Data Science Course Structure

M.Sc Data Science course structure is designed to include both core and elective subjects. The course is composed of two years divided into four semesters containing the Data Science M.Sc syllabus. M.Sc Data Science syllabus pdf is also available.

In the first year, the students are only subjected to basic knowledge through understandable subjects. While in the second year, students are introduced to specific curriculums which relate to their specialization. In addition, practical meetings and visiting sessions enhance the understanding of theoretical concepts. Thesis submission and finalizing interviews are mandatory by the end of the fourth semester as per the curriculum. The course structure is as follows:

  • IV Semesters
  • Core and Main Subjects
  • Elective and Optional Subjects
  • Practical Meet-ups and Visits
  • Research Project/ Thesis Submission

M.Sc Data Science Teaching Methodology and Techniques

The M.Sc Data Science curriculum takes into account exceptional coaching and teaching methods. Along with lectures, sensible education, and practical training, the students are trained in elective subjects of various specializations. The teaching methodology is designed to offer adaptability and communicative-based learning to the students. Listed below are the teaching methodology and techniques in general: 

  • Group and Individual Projects
  • Conceptualized Learning
  • Traditional and Unorthodox Classroom-Based Teaching
  • Practical Lab Sessions
  • Talks from guest speakers experienced in the field
  • Seminars about the scope and future
  • Semester Abroad Opportunities

M.Sc Data Science Projects

M.Sc Data Science projects are given to students for interdisciplinary and interactive learning. The M.Sc Data Science project list given here assists students in getting hands-on experience and training in educational purposes. Projects are to be completed by the end of the fourth semester. Some popular M.Sc Data Science projects list: 

  • Climate Change Impacts on the Global Food Supply.
  • Fake News Detection.
  • Human Action Recognition.
  • Forest Fire Prediction.
  • Road Lane Line Detection.
  • Recognition of Speech Emotion.
  • Gender and Age Detection with Data Science.
  • Driver Drowsiness Detection in Python.

M.Sc Data Science Books for Reference

M.Sc Data Science books are available both online and offline in many authors and publications. The M.Sc Data Science syllabus pdf which is available on the internet is meant for a better understanding of concepts. Students should put money in reference books after proper research. Some of the best M.Sc Data Science books are:

M.Sc Data Science Books



Practical Statistics for Data Scientists

Peter Bruce and Andrew Bruce

Introduction to Probability

Joseph K. Blitzstein and Jessica Hwang

Introduction to Machine Learning with Python: A Guide for Data Scientists

Andreas C. Müller and Sarah Guido

Python for Data Analysis

Wes McKinney

Python Data Science Handbook

Jake VanderPlas

R for Data Science

Hadley Wickham and Garret Grolemund

Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz and Shai Ben-David

Deep Learning

Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Mining of Massive Datasets

Jure Leskovec, Anand Rajaraman, Jeff Ullman

M.Sc Data Science Fee Structure

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