Centre for Continuing Education · IISc Bangalore
Data Science & Business Analytics
Organisations that have entered into a Memorandum of Agreement with IISc, enabling their employees to enrol in the M.Tech. (Online) programme.
M.Tech (Online) · Stream
Data Science & Business Analytics
Offered by the Division of Interdisciplinary Sciences, this programme is designed for early-career professionals with 2–8 years of experience to become technology and business leaders in information-driven enterprises.
The coursework establishes foundations of data science, trains on data engineering and machine learning techniques, and imparts practical business analysis skills — coupled with a unique industry-relevant capstone project.
Programme at a Glance
Total Programme Credits
Core Course Credits
Min Elective Credits
Project Credits
Foundation Courses
Core Courses — 12 Credits
Three mandatory core courses typically taken in the first and second semesters. These establish the foundational data science, engineering, and machine learning skills required for electives and the project.
Data Science in Practice
Real-world application of data science concepts — data collection, cleaning, exploration, modelling workflows, and communicating insights to business stakeholders.
Data Engineering at Scale
Scalable data pipelines, distributed storage, batch and stream processing, cloud data infrastructure for large-scale enterprise analytics.
Probabilistic Machine Learning
Theory and applications of probabilistic models, Bayesian inference, and uncertainty quantification in machine learning systems.
Specialisation Courses
Sample Elective Courses — Min 20 Credits
Choose from 30+ electives offered across the M.Tech. (Online) programme. Students may also take courses from the AI and ECE streams as electives. Sample DSBA electives include:
Financial Analytics
Quantitative techniques for financial modelling, risk analytics, portfolio optimisation, and decision-making in finance.
Data Mining
Pattern recognition, classification, clustering, and association rule mining for extracting knowledge from large datasets.
Tensor Computations for Data Science
Multi-dimensional data representations, tensor decompositions, and their applications in data science and machine learning.
AI for Medical Image Analysis
Deep learning and computer vision techniques applied to medical imaging, diagnostics, and clinical decision support.
Applied AI in Healthcare
Practical AI applications in healthcare systems, patient data analytics, clinical outcomes modelling, and digital health.
Quantum Computing Methods
Theory and applications of quantum computing — qubits, quantum gates, quantum algorithms, and implications for data science.
Linear Optimisation & Network Science
Linear programming, network flow problems, and graph-based models for supply chain, logistics, and operational analytics.
Capstone Work
Project — 32 Credits across 3 Phases
Students may begin the project only after successfully completing all core course credits. The project spans three consecutive terms with defined deliverables at each milestone. The final phase is an exclusive semester dedicated entirely to project completion — no courses allowed.
Phase 1 — Foundation
Identify project topic with company guide. Finalise faculty mentor. Submit approved project proposal and work plan to PCC by the drop without mention deadline.
Phase 2 — Development
Core project execution. After completing 20 credits across Phases 1 and 2, a mid-term evaluation is conducted by the faculty mentor, company guide, and one PCC-nominated faculty.
Phase 3 — Final Submission
Exclusive semester for thesis and final evaluation. No courses allowed in this term. All course requirements must be completed before Phase 3 begins.
IISc Faculty Mentor
Approves project goals, offers high-level technical and academic feedback, and coordinates mid-term and final evaluations with the PCC committee.
Company Guide
Provides active feedback and close support on the in-house project. Part of the evaluation committee appointed by the Programme Curriculum Committee (PCC).
Teaching Faculty
Departments Involved
The DSBA programme draws on faculty expertise from four IISc departments, providing a uniquely interdisciplinary perspective on data science, business, and technology.
Computational & Data Sciences (CDS)
Computational modelling, scientific computing, data-driven methods, and machine learning research.
Management Studies
Business analytics, financial modelling, operations research, and data-driven management decision-making.
CiSTUP
Centre for infrastructure, sustainable transportation and urban planning — applied data analytics for smart cities.
RBCCPS
Robert Bosch Centre for Cyber-Physical Systems — AI, autonomy, and intelligent systems research.
Programme History
Structure by Batch
The programme structure has been refined across each admission cycle. Select your batch below to see the applicable credit and course requirements.
2024 Batch Onwards — Current Structure
Core Courses — 12 Credits
- Data Science in Practice (3:1)
- Data Engineering at Scale (3:1)
- Probabilistic Machine Learning: Theory and Applications (3:1)
Elective Courses — Minimum 20 Credits
Financial Analytics, Data Mining, Tensor Computations, AI for Medical Image Analysis, Applied AI in Healthcare, Quantum Computing Methods, Linear Optimisation & Network Science, and others. Cross-stream electives from AI and ECE are also permitted.
Project — 32 Credits
3-phase project: 20 credits across Phases 1 & 2 with mid-term evaluation, then 12 credits (Phase 3, exclusive semester, final evaluation).
2023 Batch
Core Courses — 12 Credits
- Data Science in Practice (3:1)
- Applied AI: Building Practical and Scalable ML Systems (3:1)
- Data Engineering at Scale (3:1)
Elective Courses — Minimum 20 Credits
Financial Analytics, Probabilistic Machine Learning, Data Mining, AI for Medical Image Analysis, Tensor Computations, Applied AI in Healthcare, Linear Optimisation & Network Science. Students may also take electives from AI and ECE streams.
Project — 32 Credits
Same project structure: 20 credits (Phases 1 & 2, mid-term eval) + 12 credits (Phase 3, final eval).
2021 & 2022 Batch
Core Courses — 12 Credits
- Introduction to Data Science
- Introduction to Computing for AI & Machine Learning
- Data Engineering at Scale
Elective Courses — Minimum 20 Credits
Chosen from available elective offerings for the relevant year. See current sample electives above.
Project — 32 Credits
Same project structure: 20 credits (Phases 1 & 2, mid-term eval) + 12 credits (Phase 3, final eval).
Apply Now
Interested in the DSBA Stream?
Admissions for the 2026 batch are now open. Ask your organisation to nominate you — or check if your company already has an MoA with IISc.