BSc Data Science Syllabus, Subjects, Semester, Teaching Process

BSc Data Science Syllabus covers a vast array of topics and concepts related to data science. Get first-hand information about this course and subjects in this article.
authorImageVarun Saharawat30 Oct, 2025
BSc Data Science Syllabus, Subjects, Semester, Teaching Process

BSc Data Science Syllabus: The students who want to build a career in data science can take up the BSc Data Science course. It is a 3-year course that develops their understanding of various processes and concepts involved in data science. In this article, we will take a look at the BSc Data Science syllabus and subjects in detail.

The students will learn subjects like Basic statistics, C programming, introduction to statistics, linear algebra, introduction to analytics, inferential statistics, etc. during their coursework. More about the BSc Data Science syllabus has been shared in the below sections.

BSc Data Science Course, An Overview

An overview of the BSc Data Science course has been given in the below table:
BSc Data Science Course, An Overview
Particulars  Details 
Name of the course BSc Data Science
Duration 3 years
Process of admission Entrance-Based
Entrance tests AMET CET, SSU CET, and other CET taken by respective colleges or universities
Eligibility Criteria  Class XII (Science) 
Best Colleges Symbiosis, Navrachana University, IIT Madras, AMET i.e., Academy of Maritime Education and Training
Average course fees Rs. 6,00,000

BSc Data Science Syllabus

The semester-wise BSc Data Science syllabus has been given below:
BSc Data Science Syllabus
Semester 1 Semester 2 Semester 3
  • Linear Algebra
  • Communication Skills in English
  • Fundamentals of Data Science
  • Basic Statistics
  • Programming in C
  • Python Programming
  • Image Analytics
  • Machine Learning
  • Probability and Inferential Statistics
  • Introduction to Geospatial Technology
  • Discrete Mathematics
  • Advanced Python Programming for Spatial Analytics
  • Computer Organization and Architecture
  • Data Structures and Program Design in C
  • Genomics
  • Natural Language Processing
  • Research Proposal
  • Microsoft Excel Lab
  • Programming in C Lab
Semester 4 Semester 5 Semester 6
  • Programming in R Lab
  • Exploratory Data Analysis
  • Research Publication
  • Data Structure Lab
  • Data Warehousing
  • Introduction to AI (Artificial Intelligence)
  • Programming in Python Lab
  • Data Visualizations
  • Big Data Analytics
  • Machine Learning II
  • Electives 1 and 2
  • Viva
  • Project Work

BSc Data Science Subjects 

The following subjects are covered during the course:

First Year Subjects 

The subjects for the first year are mentioned below:
BSc Data Science Subjects, First Year Subjects
Subjects  Description
Linear Algebra Determinants, linear equations, linear transformations, and other topics related to mathematical structures are covered in this subject. 
Probability and Inferential Statistics The probability of a particular event or outcome determines the inferential statistics. Inferential statistics allows us to draw generalizations from a sample. 
Basic Statistics Median, mode, mean, and other central tendencies and dispersion measures are covered in this subject. 
Data Structures and Program Design in C Data structures like stack, linked list, trees, array, and more are covered in this subject. 
Computer Organization and Architecture The internal organization and working of a computer system are explained in this subject. 

Second Year Subjects 

The following subjects are included in the BSc Data Science syllabus of 2nd year:
BSc Data Science Subjects, Second Year Subjects
Subjects  Description 
Data Warehousing and Multidimensional Modeling The students learn how to represent data with data cubes in this subject. 
NLP (Natural Language Processing) The ways in which computers analyze the language and draw meaningful insights from them are discussed in this subject. 
Genomics Genetic information of a living organism along with its structure and functions are covered in this subject. 

Third Year Subjects 

The third-year syllabus includes these subjects:
BSc Data Science Subjects, Third Year Subjects
Subjects  Description 
Programming in Python Lab The fundamentals of Python Programming and its role in data science are explained in this subject. 
Data Visualizations The ways of presenting data in the form of visuals by using charts, graphs, and diagrams are discussed in this subject. 
Big Data Analytics  The various procedures of extracting trends and patterns from huge datasets are covered in this subject. 
Machine Learning II The ways in which computers learn to retrieve meanings from data are discussed in this subject.

Also read: Top 22 Data Science Companies You Should Know

BSc Data Science Syllabus of IIT Madras 

The BSc Data Science syllabus might vary slightly as per the university or college in which the students get admission. The data science syllabus of IIT Madras has been given below:
BSc Data Science Syllabus of IIT Madras 
Semester I Semester II Semester III
  • Statistics I
  • Math I
  • English I
  • Computational Thinking
  • Statistics I
  • Math I
  • English I
  • Programming in Python
  • Modern Application Development 1
  • Business Data Management
  • Database Management Systems
  • Machine Learning Foundation
  • Skill Enhancement 1
  • Programming, Data Structures and Algorithms Using Python
Semester IV Semester V Semester VI
  • Skill Enhancement 2
  • Business Analytics
  • Programming Concepts Using Java
  • Machine Learning Practice
  • Machine Learning Techniques
  • Modern Application Development 2
  • Skill Enhancement Courses
  • Strategies for Professional Growth
  • Core Courses 
  • Elective courses
  • Skill Enhancement Courses
  • Core Courses
  • Elective Courses

BSc Data Science Syllabus of Mumbai University 

The BSc Data Science syllabus of Mumbai University is given below:
BSc Data Science Syllabus of Mumbai University
Semester I Semester II Semester III
  • Introduction to Programming
  • Descriptive Statistics
  • Precalculus
  • Descriptive Statistics 
  • Precalculus Tutorials
  • Introduction to Programming Practical
  • Web Technology
  • Web Technology Practical Project 
  • Business Communication and Information Ethics
  • ICT Practical
  • Calculus
  • Environmental Science
  • Presentation on Data Science in Environmental Science
  • Probability and Distributions
  • Practical Probability and Distributions Practical
  • R Programming
  • Database Management
  • Case Studies on Microeconomics
  • Testing of Hypothesis
  • SPSS Practical
  • Tutorials On Linear Algebra and Discrete Mathematics
  • Data Warehousing
  • Linear Algebra and Discrete Mathematics
  • Microeconomics/Principles Of Management
Semester IV Semester V Semester VI
  • Data Structures
  • Data Structures Practical
  • E-Commerce and Business Ethics/Fundamentals of Accounting
  • MATLAB Practical
  • Algorithms In Data Science
  • Algorithms In Data Science Practical
  • Big Data
  • Optimization Techniques Practical
  • Optimization Techniques
  • Numerical Methods
  • Numerical Methods Practical
  • Artificial Intelligence
  • Artificial Intelligence Practical
  • Business Research Methods
  • Business Research Methods Practical
  • Data Visualisation with Power BI/Tableau
  • Data Mining
  • Data Mining Practical
  • Campus to Corporate
  • Project Dissertation
  • Electives
  • Business Forecasting
  • Business Forecasting Practical
  • Cloud Computing
  • Cloud Computing Practical
  • Internet of Things
  • Internet of Things Practical
  • Machine Learning
  • Machine Learning Practical
  • Electives
  • Project Implementation

BSc Data Science Syllabus of Andhra University 

The semester-wise BSc Data Science Syllabus of Andhra University has been given below:
BSc Data Science Syllabus of Andhra University
Semester I Semester II Semester III Semester IV
  • Math for Data Science
  • Math for Data Science Tutorial
  • Introduction to Data Science with R
  • R Programming Lab
  • Big Data Technology
  • Big Data Technology through Hadoop Lab
  • Data Mining and Data Analysis
  • Big Data Acquisition and Analysis
  • Big Data Acquisition and Analysis Lab

BSc Data Science Syllabus of Osmania University 

The semester-wise BSc Data Science Syllabus of Osmania University has been given below:
BSc Data Science Syllabus of Osmania University 
Semester I Semester II Semester III
  • Fundamentals of Information Technology
  • Fundamentals of Information Technology (Lab)
  • Problem solving and Python Programming
  • Problem solving and Python Programming (Lab)
  • University Specified Subjects
  • Mini Project 
  • Data Engineering with Python 
  • Data Engineering with Python (Lab)
Semester IV Semester V Semester VI
  • Machine Learning (Lab)
  • Machine Learning
  • Mini Project
  • University Specified Subjects
  • Data Structures and Algorithms
  • No SQL Databases Lab
  • Natural Language Processing Lab
  • No SQL Databases
  • Natural Language Processing
  • Deep Learning
  • Big Data
  • Deep Learning Lab
  • Big Data Lab
  • Project (Major)

BSc Data Science Syllabus, Teaching Process 

The BSc Data Science syllabus includes theoretical and practical subjects. So, the syllabus is covered through different teaching methods. These methods include lectures and practicals along with group discussions, seminars, and research papers. The students also get internships after completing their coursework. It enhances their practical experience and knowledge. 

Recommended Course

BSc Data Science Syllabus, Main Books 

The books that the students can refer to cover the BSc Data Science syllabus have been mentioned in the below table:
BSc Data Science Syllabus, Main Books
Book Name Author Name Details
R for Data Science Garret Grolemund and Hadley Wickham This book teaches how to conduct data science processes by using R. 
Understanding Machine Learning: From Theory to Algorithms Shai Ben David and Shai Shalev-Shwartz The fundamental ideas of machine learning and related mathematical derivations are covered in this book.
Python Data Science Handbook Jake VanderPlus The fundamentals of Python and their application in data science are explained in this book.
Python For Data Analysis Wes McKinney The data science tools that Python offers are explained by the author through this book. 
These were some of the key parts and components of the BSc Data Science syllabus. It is not mandatory to complete this course for pursuing a career in the data science field. The students can also increase their data analytical and programming skills by undertaking various data science certifications and online courses.  PW Skills offers various data analytics courses. You can take up these courses to expand your skills and knowledge in data science. Apart from the course syllabus, you also get job assurance and career guidance from industry experts. Access to study materials, instructor-led sessions, and other learning sources enhances your career prospects and knowledge. Visit our portal today and explore the best data analytics courses!
Our Social Channels

BSc Data Science Syllabus (FAQs)

What is BSc. Data Science Course?

BSc. Data Science is a 3-year degree course that covers the vast principles and subjects associated with data science. It is meant for the students who are interested to pursue a career in data science.

Is BSc data science hard?

The students who do not have sufficient foundation in statistics, computer programming, and mathematics can find it difficult.

Is coding taught in BSc Data Science?

Yes, the BSc Data Science syllabus includes coding in C, Python, R, and other programming languages. So, the students will learn a fair bit of coding during their coursework. Yet, the primary focus of the syllabus is to teach various concepts and processes of data science.

Is BCA Data Science better than BSc Data Science?

BCA Data Science is more focussed on the computer applications used in the field of data science. BSc Data Science will give the students an idea of the core concepts of data science. So, both the courses are beneficial, and students must choose them as per their preferences.

Which course is better: BSc Data Science or B. Tech Data Science?

The syllabus and core subjects of both these courses are similar. However, B. Tech Data Science is a 4-year course and much more recognized by top IT companies. So, the students will get more industry-relevant exposure and opportunities by completing this course.

What are the benefits of BSc Data Science?

The vast BSc Data Science syllabus helps the students to grasp concepts related to data engineering, database administration, data analytics, and more. It makes them acquainted with data mining, processing, data visualisation, and data analysis tasks which are in demand in healthcare, e-commerce, IT, and many other business verticals.

Is C++ required for data science?

C++ programming is related to data structures and functions. So, learning it will help the students while handling complex computations in data science.

Which language is the best for data science?

Python and R are the two most used programming languages in data science. So, learning these languages can help the students in enhancing their skills in handling various data science tasks.

Does BSc Data Science guarantee a bright future?

Various entry-level positions as a data analyst or researcher may be available after completing the BSc Data Science course. However, the students must complete data science certifications from renowned institutes to improve their portfolio.

Who is eligible for a data science course?

The students who have completed their 12th class in the Science stream are eligible for the data science course. However, they must also possess sufficient knowledge about computers and must acquire the fundamentals of math, statistics, and computer science before applying.

Which course is the best: BSc Data Science or BSc Computer Science?

BSc Computer Science is for the students who want to pursue a career in the field of software development or testing. Those who want to pursue a career in the field of data analytics should go for the BSc Data Science course.

How much coding is required in data science?

One can survive in the field of data science with the beginner-level programming knowledge in either Python or R. The students must also possess some knowledge about database management languages like SQL to excel in this field.