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BSc Data Science Syllabus and Subjects Explained
04-03-2026
Have you ever thought about how Netflix knows what you want to watch next? Or how companies predict what customers will buy?
The answer to the above question lies in Data Science. In 2026, data science is one of the strongest and most in-demand solutions.
Data Science means applying statistics, research, mathematics and data analysis to find meaningful insights from the data.
Data Science is an interdisciplinary area associated with large-scale data to extrapolate data that can be useful to make meaningful decisions.
By 2026, the discipline will gather greater momentum and use Artificial Intelligence and Machine Learning to answer complex real-world problems.
If you major in Data Science, you will be able to pursue excellent job opportunities in finance, healthcare, e-commerce and technology.
Understanding the Bachelor in Data Science (BSc) Data Science syllabus and subjects will help you see how this course prepares you for these exciting opportunities.
Let us explore the BSc Data Science subjects in detail.
BSc Data Science Course Overview
A BSc in Data Science is a four-year undergraduate programme that helps you understand the basic fundamentals of collecting data from multiple sources.
You will learn how to extract valuable insights and knowledge from large amounts of data to help solve business problems. You will use this data to come up with smart statistical models and datasets to meet business requirements.
During the course, you will learn exploratory data analysis and data modelling using various programming tools and technologies. Machine learning techniques are also an important part of the curriculum.
The course also focuses strongly on data visualisation, as well as the deployment and maintenance of databases. The BSc data science course details have been discussed below:
| Course Name | Bachelor of Science (BSc) in Data Science |
| Level of Education | Undergraduate |
| Duration | Four years (8 semesters) |
| Eligibility | Passed 10+2 with at least 50% marks from a recognised stream in the Science stream |
| Admission Process | Merit or entrance-based |
| Major Data Science Specialisations |
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| Job Roles After Graduation |
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BSc Data Science Eligibility
To be eligible for the BSc Data Science course, you must pass 10+2 from a recognised board in the Science stream with at least 50% marks. You must have a background in PCM (Physics, Chemistry and Mathematics) in 10+2.
While not mandatory, having Computer Science as one of the subjects in 10+2 can be an added advantage.
Additionally, you must pass national, state, or college-specific entrance exams to secure a seat in the top colleges of the country. Some of the top entrance exams are as follows:
| Entrance Exam | Full Form | Level |
| JEE | Joint Entrance Examination | National |
| COMEDK UGET | Consortium of Medical, Engineering and Dental Colleges of Karnataka Undergraduate Entrance Test | State |
| KCET | Karnataka Common Entrance Test | State |
| CUET | Common University Entrance Test | National |
BSc Data Science Entrance Exam Syllabus
The entrance examination may assess candidates across aptitude, mathematics, science fundamentals, English proficiency, logical reasoning, and basic computer knowledge.
Here is an indicative syllabus list; the actual entrance exam syllabus may vary depending on the authority or the institution conducting the exam.
| Section | Area Covered | Key Topics Included |
| A. Reading Comprehension | English Language Skills |
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| B. Subject-Based | Core Subject Knowledge |
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| C. Basic Mathematics | Numerical Ability |
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| D. General Knowledge & Current Affairs | General Awareness |
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BSc Data Science Syllabus
The BSc Data Science syllabus uses the basic concepts of statistics, data analysis, machine learning and related subjects to solve the major problems through real-time data analysis.
We have provided a semester-wise breakdown of the BSc Data Science subjects:
| Semester-I | Semester-II |
| Relational Database Management System | Data Analysis Using Python |
| Mathematical Foundation for Data Science I | Mathematical Foundation for Data Science II |
| Minor Subject | Minor Subject |
| AEC1 | AEC2 |
| Database Management System Lab | Data Analysis Using Python Lab |
| Mathematical Foundation for Data Science I Lab | Mathematical Foundation for Data Science II Lab |
| General Elective | General Elective |
| Mind Management and Human Values 1 | Mind Management and Human Values 2 |
| Transdisciplinary Project Centric Learning 1 | Transdisciplinary Project Centric Learning 2 |
| Semester-III | Semester-IV |
| Fundamentals of Statistics and Probability | Data Visualisation and Interpretation |
| Data Analysis with R / Data Warehousing & Data Mining | Artificial Intelligence |
| Minor Subject (DSC) | Predictive Modelling / Cloud Computing and Data Security |
| Minor (DSE) | Minor Subject |
| AEC3 | AEC4 |
| Fundamentals of Statistics and Probability Lab | Data Visualisation and Interpretation Lab |
| Data Analysis with R Lab / Data Warehousing & Data Mining Lab | Artificial Intelligence Lab |
| VAC1 | Predictive Modelling Lab / Cloud Computing and Data Security Lab |
| Transdisciplinary Project Centric Learning 3 | VAC3 |
| Transdisciplinary Project Centric Learning 4 | |
| Semester-V | Semester-VI |
| Machine Learning | Big Data Analytics |
| Inference Theory / Information Retrieval | Time Series Analysis / High Performance Computing and Distributed Systems |
| Graph Theory / Cryptography & Network Security | Numerical Methods for Data Science / Deep Learning and Neural Network |
| Minor Subject | Minor Subject |
| Machine Learning Lab | Big Data Analytics Lab |
| Inference Theory Lab / Information Retrieval Lab | Time Series Analysis Lab / High Performance Computing and Distributed Systems Lab |
| Graph Theory Lab / Cryptography & Network Security Lab | Numerical Methods for Data Science Lab / Deep Learning and Neural Network Lab |
| Project | Transdisciplinary Project Centric Learning 6 |
| Internship | Project |
| Transdisciplinary Project Centric Learning 5 | |
| Semester-VII | Semester-VIII |
| Honours | Honours |
| Natural Language Processing | Generative AI |
| Computer Vision and Image Analytics / Applied Multivariate Statistics | Applied Machine Learning / Text and Web Mining |
| Reliability & Survival Analysis / IoT and Data Analytics | Optimisation Techniques / Virtual and Augmented Reality |
| Minor Subject | Minor Subject |
| General Elective | Generative AI Lab |
| Natural Language Processing Lab | Applied Machine Learning Lab / Text and Web Mining Lab |
| Project | Project |
| Honours with Research | Honours with Research |
| Natural Language Processing | Generative AI |
| Computer Vision and Image Analytics / Applied Multivariate Statistics | Research Methodology for Data Science / AI Ethics & Responsible Data Science |
| Reliability & Survival Analysis / Reinforcement Learning & Optimisation Techniques | Text and Web Mining / Applied Machine Learning |
| Minor Subject | Minor Subject |
| General Elective | Generative AI Lab |
| Natural Language Processing Lab | Text and Web Mining Lab / Applied Machine Learning Lab |
| Project | Project |
Wrapping Up
Data Science stands at the centre of innovation, powering decisions across various industries.
A BSc in Data Science helps you develop strong foundations in statistics, programming, machine learning, data visualisation, and advanced tools like Artificial Intelligence and Big Data Analytics.
With pra ctical labs, internships, and research opportunities, the course prepares you for high-demand roles such as Data Analyst and Business Intelligence Analyst.
If you are passionate about technology and problem-solving, now is the perfect time to tap into opportunities. Explore top universities, prepare for entrance exams, and enrol in BSc Data Science to secure a future-ready career.
FAQs
Q1. What is BSc in Data Science?
A1: A BSc in Data Science is an undergraduate programme that focuses on data analysis, statistics, programming, and machine learning.
It trains you to collect, process, and interpret large datasets to support data-driven decision-making.
Q2. Can I study BSc Data Science without Maths?
A2: Most universities require Mathematics as a subject in 10+2 because it is essential for statistics and machine learning concepts. However, eligibility criteria may vary across institutions.
Q3. Is BSc Data Science very difficult?
A3: The course can be challenging as it involves Mathematics, programming, and analytical thinking. With consistent practice and interest in problem-solving, it becomes manageable and rewarding.