School of Engineering and Technology
B.Tech (Data Science)
A programme built for the age of data – covering the full data pipeline from ingestion to business intelligence, with a strong statistical and computational foundation, and domain electives across healthcare, finance, and enterprise analytics.
About the Programme
Data scientists are the most sought-after professionals of this decade – and this programme trains students to be exceptional ones. The curriculum covers the mathematical foundations of statistical inference, the engineering challenges of big data pipelines, and the domain expertise needed to turn numbers into decisions. Students work with real datasets every semester – Kaggle, government open data, and industry-provided datasets from partner companies. Graduates emerge with a portfolio of end-to-end data projects, proficiency in Python and R, and certifications from Databricks, Tableau, or AWS.
Programme Details
Course at a Glance
180
8 Semesters
Data Science
60
Engineering and Technology
Programme Structure
YEAR I - DATA FOUNDATIONS
Statistics, Python, and SQL
Statistics and Probability · Python for Data Science ·SQL and Databases · AI Literacy · Data Visualisation Fundamentals · Communication Skills · Human Values
YEAR II - DATA ENGINEERING
Building data pipelines
R for Statistical Computing · Data Structures and Algorithms · Machine Learning · Big Data Technologies (Hadoop, Spark) · DBMS and NoSQL · Data Warehousing · Business Analytics Fundamentals
YEAR III - ADVANCED ANALYTICS
From data to decisions
Deep Learning for Tabular Data · Time Series Analysis · NLP for Data Science · Cloud Data Platforms · Data Engineering at Scale · 2 Domain Electives · Internship (mandatory)
YEAR IV - CAPSTONE
Portfolio and professional launch
Capstone Data Science Project (two semesters) · Advanced Electives · Research Methods · Data Ethics and Privacy · Business Communication for Data Scientists
Specialisations - 5 Tracks
BUSINESS INTELLIGENCE AND VISUALISATION
Tableau · Power BI · Executive Dashboards · KPI Design · Data Storytelling
BIG DATA ENGINEERING
Apache Spark · Kafka · Hadoop · ETL Pipelines · Data Lakes · Apache Airflow
HEALTHCARE ANALYTICS
Clinical Data · Electronic Health Records · Epidemiology · Drug Trial Analytics
FINANCIAL ANALYTICS
Risk Modelling · Fraud Detection · Quantitative Finance · Regulatory Data
AI AND PREDICTION
Forecasting · Recommendation Systems · Anomaly D · Auto ML
Themes
Full Data Science Stack
From data collection and cleaning (pandas, SQL) through analysis (R, scipy) to deployment (FastAPI, Streamlit). Students graduate with end-to-end project experience on real data.
Real datasets every semester
No synthetic problems. Every semester includes a project on real-world datasets from Kaggle, government open data portals, and industry partners. Portfolio-building from I Year.
Data ethics as a core subject
Data privacy, DPDPA 2023, GDPR, algorithmic bias, and fairness are taught as core subjects—not optional modules. Responsible data use is non-negotiable.
Curriculum Overview
Data-first curriculum from Day 1
Mathematics, programming, statistics, and data analysis are integrated from Semester I. Students begin working with Python, SQL, Excel, and real datasets to build practical analytical skills from the very first year.
Python, SQL & Analytics Labs
Hands-on labs cover Python, SQL, Pandas, Power BI, and Tableau for data cleaning, visualization, and analysis. Students build practical dashboards and analytics projects using industry-standard tools.
Real-world datasets & industry projects
Students work on real datasets from healthcare, finance, retail, and public data sources throughout the program. Capstone projects focus on solving practical business problems with data-driven insights.
AI, Big Data & Business Intelligence
The curriculum introduces Machine Learning, Big Data concepts, and Business Intelligence as core learning areas. Students learn how to transform raw data into meaningful insights that support real-world decision-making.
Eligibility Criteria
| Criterion | Requirement |
|---|---|
Qualifying exam | 10+2 (Class XII) from any recognised board |
Mandatory subjects | Physics, Chemistry, Mathematics (PCM) |
Minimum marks | 60% aggregate in PCM (55% for SC / ST / PwD) |
Entrance exam | Valid JEE Main score OR Metro University MET |
Preferred | Students with strong Mathematics background, computing interest, or Olympiad exposure |
Selection | JEE Main / MET → Counselling → Seat allotment |
Frequently Asked Questions
How is B.Tech Data Science different from B.Tech CSE?
B.Tech Data Science focuses on data analytics, statistics, machine learning, and business intelligence alongside core computing fundamentals. Students learn to collect, analyze, visualize, and interpret data to solve real-world problems across industries.
Do I need prior coding or Data Science knowledge?
No. The programme is designed for beginners, with Python, SQL, statistics, and data analysis introduced from the first semester. Students gradually build practical skills through hands-on labs and real-world projects.
What career opportunities are available after graduation?
Graduates can pursue roles such as Data Analyst, Business Intelligence Analyst, Data Scientist, Machine Learning Engineer, Analytics Consultant, and Big Data Associate across industries including healthcare, finance, retail, and technology.
Will I work on real-world datasets during the programme?
Yes, students can join faculty research groups. Year IV project can be co-supervised by an industry researcher. Conference publications and patent support are actively provided.Yes. Students gain practical experience through projects using real-world datasets, visualization tools, analytics platforms, and capstone projects that prepare them for industry-ready data-driven decision-making.