B.Sc Data Science Subjects and Year-Wise Syllabus
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- 1 Oct 2026LPUNEST 2027 Registration Ongoing
LPUNEST 2027 entrance registration for B.Sc Data Science admissions is open and candidates can apply till Jan 15, 2027.
B.Sc Data Science is a 3 to 4-year undergraduate degree combining Mathematics, Statistics, Computer Science, and Data Analytics. The course covers core subjects such as Linear Algebra, Programming in C, Python, R, Data Structures, Machine Learning, Big Data Analytics, and Data Visualization across 6 to 8 semesters.
| Parameter | Details |
|---|---|
| Course Duration | 3 Years to 4 Years (6 to 8 Semesters) |
| Eligibility Criteria | Passed 10+2 in Science stream from a recognized board with minimum 50% aggregate marks (Mathematics required by select universities) |
| Admission Process | Merit-based or University Entrance Exams (SSU CET, AMET CET, Jain Entrance Test, KR Mangalam Entrance Test, LPUNEST) |
| Core Skill Areas | Python, R, C Programming, Linear Algebra, Machine Learning, Big Data Analytics, SQL/NoSQL Databases, Data Visualization |
| Average Graduate Salary | Rs 6.4 LPA to Rs 14.4 LPA depending on job role |
B.Sc Data Science Subjects Year-wise
The curriculum of B.Sc Data Science progresses systematically from foundational computing and mathematical concepts in the first year to advanced data engineering, artificial intelligence, and predictive modelling in the third year.
First-Year Subjects
Computer programming modules introduce Programming in C, Python Programming, and Computing Fundamentals. Statistical training covers Basic Statistics, Descriptive Statistics, and Probability and Inferential Statistics.
| Subject Name | Domain | Key Topics Covered |
|---|---|---|
| Linear Algebra | Mathematics | Systems of linear equations, vector spaces, determinants, matrix operations, and linear transformations. |
| Basic & Descriptive Statistics | Statistics | Measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation). |
| Programming in C | Computer Science | Syntax, control flow, functions, memory allocation, and array handling. |
| Data Structures & Program Design | Computer Science | Stacks, queues, linked lists, trees, graphs, and algorithmic efficiency. |
| Probability & Inferential Statistics | Statistics | Probability distributions, random variables, hypothesis testing, and outcome estimations. |
| Fundamentals of Data Science | Core Data Science | Data collection concepts, data cleaning workflows, data lifecycle, and domain applications. |
Second-Year Subjects
Second-year subjects transition into database management and pattern discovery. Students cover Data Warehousing & Multidimensional Modeling, Exploratory Data Analysis (EDA), Natural Language Processing (NLP), Relational Database Management Systems (RDBMS), and Genomics or Domain Analytics.
| Subject Name | Year / Semester | Core Learning Objective |
|---|---|---|
| Data Warehousing & Multidimensional Modeling | Year 2 / Sem IV | Representing data cubes, defining dimensions and facts for enterprise analytics. |
| Exploratory Data Analysis (EDA) | Year 2 / Sem IV | Pattern discovery, anomaly detection, statistical summary techniques, and hypothesis checks. |
| Natural Language Processing (NLP) | Year 2 / Sem III | Processing human language, text mining, tokenization, sentiment analysis, and syntax parsing. |
| Relational Database Management Systems (RDBMS) | Year 2 / Sem IV | SQL querying, schema normalization, database transactions, and data management. |
| Genomics / Domain Analytics | Year 2 / Sem III | Application of data models to genetic/biological sequences and health data. |
Third-Year Subjects
Third-year advanced subjects cover Machine Learning II, Big Data Analytics, Data Visualization, Cloud Computing, Artificial Intelligence, and Deep Learning, preparing students to handle enterprise-grade data challenges.
| Subject Name | Year / Semester | Focus & Tools Covered |
|---|---|---|
| Machine Learning II | Year 3 / Sem V | Supervised/unsupervised algorithms, neural networks, predictive modelling, model evaluation. |
| Data Visualization | Year 3 / Sem VI | Graphical representation of data insights using tools like Power BI, Tableau, and Matplotlib. |
| Cloud Computing | Year 3 / Sem VI | Distributed computing resources, cloud storage, deployment of machine learning pipelines. |
| Artificial Intelligence & Deep Learning | Year 3 / Sem V-VI | Neural network architectures, automated decision systems, and cognitive computing. |
B.Sc Data Science Electives and Specialisations
Universities offer specialised electives allowing students to align their degree with specific industry applications like business forecasting, cloud analytics, or data engineering.
Curriculum design varies across top higher education institutions in India:
- IIT Madras (BS Data Science): Focuses on Computational Thinking, Modern Application Development, Machine Learning Practice, and Business Data Management.
- Mumbai University: Offers applied modules such as Testing of Hypothesis, Optimization Techniques, E-Commerce Ethics, Business Forecasting, Microeconomics, Campus to Corporate, and Data Visualization with Power BI and Tableau.
- Osmania University: Emphasises Fundamentals of IT, Data Engineering with Python, NoSQL Databases, Deep Learning, and Mini/Major Projects.
- Kristu Jayanti College (KJC) Bangalore: Teaches Computing Fundamentals, OOPs with Java, Digital Analytics, and Artificial Neural Networks.
| University / Institute | Unique Subjects Offered | Key Focus Areas |
|---|---|---|
| IIT Madras (BS Data Science) | Computational Thinking, Modern Application Development, Machine Learning Practice, Business Data Management | Application development, programming logic, machine learning practice. |
| Mumbai University | Testing of Hypothesis, Optimization Techniques, E-Commerce Ethics, Business Forecasting, Tableau/Power BI | Applied statistics, business analytics, and visualization tools. |
| Osmania University | Fundamentals of IT, NoSQL Databases, Deep Learning, Data Engineering with Python | Data engineering, NoSQL systems, and deep learning models. |
| Kristu Jayanti College (KJC) Bangalore | Computing Fundamentals, OOPs with Java, Digital Analytics, Artificial Neural Networks | Object-oriented programming, neural networks, and web/digital analytics. |
B.Sc Data Science Practicals and Projects
Practical laboratory training is integral to every semester of B.Sc Data Science. Practical modules reinforce core theory by training students to build algorithms, process raw data, and create visual analytics dashboards using real-world datasets.
Key lab courses included throughout the curriculum are:
- C Programming Practical and Data Structures in C Practical
- ICT / Excel Lab and Microsoft Excel Lab
- Advanced Python Practical and Programming in Python Lab
- Programming in C Lab and Java Lab
- Programming in R Lab and RDBMS Practical
- Big Data Hadoop Lab and Big Data Analytics Practical
- Data Visualization Practical
In the final semester, students complete a Major Project & Grand Viva. This capstone project requires building end-to-end data processing pipelines, training predictive machine learning models, and presenting actionable business insights to an evaluation panel.
Books for B.Sc Data Science
| Book Title | Author(s) |
|---|---|
| Python for Data Analysis | Wes McKinney |
| Python Data Science Handbook | Jake VanderPlas |
| Understanding Machine Learning: From Theory to Algorithms | Shai Shalev-Shwartz & Shai Ben-David |
| R for Data Science | Hadley Wickham & Garret Grolemund |
Recommended reference books cover key programming languages taught in B.Sc Data Science, including Python and R. The course curriculum blends subjects from computer science, statistics, and mathematics.
Career Scope, Job Profiles, and Salary Expectations
- Business Intelligence Specialist: Earns an average salary of Rs 14 LPA.
- Data Engineer: Earns an average salary of Rs 10.8 LPA.
- Data Visualisation Specialist: Earns an average salary of Rs 8 LPA.
- Data Analyst: Earns an average salary of Rs 6.4 LPA.
Top recruiting companies hiring B.Sc Data Science graduates include Amazon, Google, Microsoft, Deloitte, IBM, McKinsey & Company, BCG, Goldman Sachs, and MuSigma.
How to Apply for B.Sc Data Science Admission in 2026: Step by Step
- Check Eligibility Criteria: Candidates must pass Class 12 in the Science stream from a recognized board with at least 50% aggregate marks, including Mathematics if required by the university.
- Apply for Entrance Exams: Register for university-level entrance tests such as SSU CET, AMET CET, Jain University Entrance Test, or KR Mangalam Entrance Test.
- Submit Application Form: Complete the online or offline application form for target institutions such as NMIMS, Navrachana University, or The Northcap University.
- Attend Counselling and Submit Documents: Shortlisted candidates must attend counselling sessions, submit all required academic documents, and pay the course fee to confirm admission.
| What to do | Where | Link |
|---|---|---|
| Compare colleges, fees and cutoffs | B.Sc Data Science colleges in India | findmycollege.com |
Admissions are conducted through university-level entrance exams or merit, followed by document submission and fee payment during counselling.
FAQs
How many subjects are in BSc data science?
A typical B.Sc Data Science degree spans 3 to 4 years, incorporating core theory papers, laboratory practicals, electives, and project work covering Mathematics, Statistics, Computer Science, and Data Analytics.
Does B.Sc data science need maths?
Yes, Mathematics is a foundational pillar of B.Sc Data Science. Core mathematical subjects include Linear Algebra, Calculus, Precalculus, Discrete Mathematics, and Descriptive/Inferential Statistics.
Is B.Sc data science difficult?
B.Sc Data Science combines programming (Python, C, R) with quantitative mathematics and statistics. While analytical and technical, students with a strong interest in logic, mathematics, and computing can comfortably master the curriculum.
Is BSc data science better than BCA?
B.Sc Data Science provides specialized training in data mining, statistics, machine learning, and AI, preparing students directly for roles like Data Scientist and Data Engineer.
Is BSc data science has scope?
Yes, B.Sc Data Science has strong industry scope with top recruiters like Amazon, Google, Deloitte, McKinsey, and IBM.
What are the subjects in BSc Data Science 1st year?
First-year subjects include Linear Algebra, Basic Statistics, Programming in C, Fundamentals of Data Science, Data Structures, Computer Architecture, Discrete Mathematics, and Probability & Inferential Statistics alongside practical lab modules.
