IIT Madras BS Data Science: your career hides in a few elective choices.

Frame a business problem as a modelling task, clean and explore data, train and evaluate models, then hand the model to engineering or deploy it yourself.

Modern DS roles expect neural-network fluency. Option 2 also keeps Business Analytics open as a later elective.

Strongly recommended. One real end-to-end ML project is the strongest resume item here. Prefer it over a third optional elective.

Use NPTEL (max 8 credits total) for gaps your term skips, like time-series. Don't lean on it for core skills.

1. Diploma: take Option 2 and finish ML Foundations → Techniques → Practice in order.
2. BSc: add Linear Statistical Models and Algorithms for Data Science.
3. BS: after core Deep Learning, take Deep Learning Practice, Data Science & AI Lab and MLOps; satisfy 2 BP courses + HS/MG.
4. Spend your final slot on an apprenticeship doing real model work.

Pull data with SQL, build dashboards, run analyses to answer stakeholder questions, and present findings simply to non-technical teams.

Business Analytics is the spine of the analyst role. (Note: choosing Option 1 closes off the Intro DL & GenAI course later.)

Recommended if you can land an analytics role. Otherwise a portfolio of strong SQL + dashboard projects works for first jobs.

NPTEL (max 8 credits) is good for tool depth like Tableau, Power BI or advanced SQL.

1. Diploma: take Option 1 (Business Analytics) after Business Data Management.
2. BSc/BS: add Data Visualization Design, Market Research and Managerial Economics.
3. Cover the mandatory BP + BD Level-4 courses with the lighter options you enjoy.
4. Build 2–3 dashboard projects and add an analytics apprenticeship if available.

Write frontend and backend code, design APIs and databases, test and debug, and deploy software to production.

Either option works. Option 2 keeps more AI electives open; Option 1 is lighter if you want all credits on systems courses.

Strongly recommended. A shipped app or real engineering internship outweighs extra theory electives for this role.

NPTEL (max 8 credits) is useful for DevOps, cloud or a specific framework.

1. Use the Diploma's DBMS, Java, PDSA, MAD I & II as your base.
2. Take Programming in C early — it unlocks the Computer Systems minor.
3. BS: after core Software Engineering + Testing, take App Dev Lab, Operating Systems and Computer Networks (these three are the minor).
4. Cover 2 BD Level-4 courses + HS/MG, then do an engineering apprenticeship.

Prompt or fine-tune LLMs, build retrieval pipelines, integrate models into products, and evaluate and ship GenAI features.

Deep Learning & GenAI is non-negotiable for this track, and it keeps every AI elective open.

Strongly recommended at an AI-focused team. Ship one real GenAI project (a working RAG app, a fine-tuned model) you can demo.

NPTEL (max 8 credits) helps for niche topics like diffusion models or RLHF.

1. Diploma: take Option 2.
2. BS: master core Deep Learning, then Deep Learning Practice (unlocks the Multimodal minor).
3. Stack LLMs + Math Foundations of GenAI (Generative AI minor) then DL for CV + Speech Technology (Multimodal minor).
4. Add 2 BP Level-4 + HS/MG, then an AI apprenticeship; consider PGD → MTech.

Design data pipelines, manage databases and warehouses, and keep data clean, available and fast for analysts and scientists.

Either option. Option 2 pairs better with the ML-adjacent Cloud Computing minor.

Recommended. Real data-infrastructure experience is hard to demonstrate from coursework alone.

NPTEL (max 8 credits) is strong for Spark, Kafka and cloud-tooling depth.

1. Lean on Diploma DBMS and System Commands.
2. BS: take Introduction to Big Data, MLOps and Deep Learning Practice (the Cloud Computing minor).
3. Add Operating Systems and Computer Networks for the BP requirement and systems depth.
4. Cover HS/MG, then do a pipeline-focused apprenticeship.

Automate model training and deployment, monitor models in production, and build the infrastructure data scientists depend on.

Deep Learning & GenAI, so you understand the models you operate.

Strongly recommended. This role is defined by hands-on production experience more than any other on this list.

NPTEL (max 8 credits) helps for Docker, Kubernetes and CI/CD depth.

1. Diploma: take Option 2 and finish ML Practice.
2. BS: use core Software Engineering + Testing as your base.
3. Take MLOps, Introduction to Big Data and Deep Learning Practice (Cloud Computing minor), plus App Dev Lab for the BP slot.
4. Add HS/MG, then a production-focused apprenticeship deploying real models.

Read papers, formalise problems, run careful experiments, and aim for an MS, PhD, or the IITM MTech research project.

Deep Learning & GenAI gives research-relevant ML grounding and keeps options open.

Use the 12-credit apprenticeship slot for the 8-month MS-by-research project at the Dept of DSAI if you reach CGPA 8.0. A research output matters more than a generic internship.

NPTEL (max 8 credits) is good for advanced maths — optimization, convex analysis — that strengthens a research profile.

1. Build maths hard from the Foundation level and guard your CGPA.
2. BSc/BS: take Discrete Mathematics, Theory of Computation and Advanced Algorithms.
3. Add Algorithms for Data Science, Reinforcement Learning and Sequential Decision Making (the Algorithmic Decision Making minor).
4. Aim for the MS-by-research upgrade (CGPA 8.0+) or sit GATE; or upgrade online to PGD → MTech.

Build financial models, analyse risk, detect fraud and anomalies, and support trading or finance decisions with data.

Business Analytics gives the business-data grounding this work needs.

Recommended at a fintech, bank or analytics firm where you touch real financial data.

NPTEL (max 8 credits) is useful for financial econometrics or derivatives pricing.

1. Diploma: take Option 1 (Business Analytics).
2. BS: take Corporate Finance, Managerial Economics and Game Theory (the Economics & Finance minor).
3. Add Financial Forensics and Linear Statistical Models for modelling muscle.
4. Cover the BP + BD stream rule, then apprentice at a finance or fintech team.

Analyse biological or clinical data, model molecular and network problems, and support research and decisions in the life sciences.

Deep Learning & GenAI supports modern bio-ML methods.

No single minor maps cleanly to this track. Optimise electives for relevance instead of a credential.

Strongly recommended at a pharma, biotech or research lab — directly relevant to life-sciences analytics and consulting roles.

NPTEL (max 8 credits) is good for genomics, computational biology or biostatistics.

1. Diploma: take Option 2 and finish ML Practice.
2. BS: take Algorithmic Thinking in Bioinformatics and Big Data & Biological Networks.
3. Add Statistical Computing, the core Deep Learning, and cover 2 BP Level-4 + HS/MG.
4. Apprentice in a life-sciences or pharma setting.

Gather requirements, analyse user and market data, define features, and work between engineering and business teams.

Business Analytics gives the business-decision grounding.

Recommended in a product or strategy role where you own a real feature or analysis.

NPTEL (max 8 credits) is useful for product management or UX foundations.

1. Diploma: take Option 1 (Business Analytics).
2. BS: take Design Thinking for Data-Driven Apps, Market Research and Industry 4.0.
3. Add the Economics & Finance minor courses to cover HS/MG and round out business sense.
4. Cover the BP + BD stream rule, then do a product internship.

Own uptime SLOs, respond to incidents, automate toil, and design the infrastructure that keeps services running when traffic spikes or servers fail.

Either works. Option 2 pairs well if you also want to understand the ML workloads you operate.

Strongly recommended at a startup or cloud-scale company. On-call experience and real incident postmortems are worth more than any elective on a SRE resume.

NPTEL (max 8 credits) is strong for distributed systems, cloud infrastructure and Kubernetes administration.

1. Diploma: finish System Commands and DBMS — they are the Linux/SQL baseline.
2. BSc/BS: take Programming in C early to unlock the Computer Systems minor.
3. BS: stack Operating Systems + Computer Networks + Computer System Design (the minor), then add MLOps for pipeline automation.
4. Cover BD Level-4 + HS/MG, then do a platform or infrastructure apprenticeship.

Write CI/CD pipelines, manage container orchestration, provision cloud infrastructure as code, and close the loop between development and operations.

Either works. Option 2 opens the Cloud Computing for AI minor, which maps directly to DevOps tooling in this programme.

Strongly recommended. Real pipeline ownership — shipping code to prod, managing containers, writing infrastructure-as-code — defines this role more than any coursework.

NPTEL (max 8 credits) is excellent for Docker, Kubernetes, Terraform and AWS/GCP cloud certifications.

1. Diploma: complete MAD I & II — web app fundamentals are the base for App Dev Lab.
2. BS: take App Dev Lab (BP Level-4) and MLOps (BD Level-4) together — these two define the DevOps workflow.
3. Add Operating Systems + Computer Networks for systems depth; take Introduction to Big Data + Deep Learning Practice to earn the Cloud Computing for AI minor.
4. Cover HS/MG, then do an apprenticeship owning a real CI/CD pipeline end to end.

Run sprint planning, manage stakeholder expectations, track risks and dependencies, write PRDs, and keep cross-functional teams aligned from kickoff to launch.

Business Analytics gives the data-driven decision-making foundation PMs need for roadmap prioritisation and metrics reporting.

Strongly recommended. Running a real project with actual stakeholders — even a student initiative or open-source repo — is the PM credential that matters most. Prefer this over extra electives.

NPTEL (max 8 credits) is useful for Agile/Scrum foundations, PMP preparation, or supply-chain and operations management.

1. Diploma: take Option 1 (Business Analytics) after Business Data Management.
2. BS: take Design Thinking for Data-Driven Apps, Industry 4.0 and Market Research early.
3. Add Game Theory & Strategy + Managerial Economics + Corporate Finance to earn the Economics & Finance minor and cover HS/MG.
4. Cover 2 BP + 2 BD Level-4 (Data Visualization Design for BP; one light BD elective), then do a PM-role apprenticeship.