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BS in Data Science and Applications

Indian Institute of Technology, Madras – Pursuing (2025 – Present)

The programme is offered in a flexible online mode, allowing exit after Foundation, Diploma, or Degree levels. It blends mathematics, statistics, programming, and machine learning.

Foundation Level

  • Computational Thinking – Problem decomposition, pseudocode, algorithmic reasoning, recursion.
  • Mathematics for Data Science 1 – Sets, functions, sequences, basic calculus.
  • Mathematics for Data Science 2 – Linear algebra: matrices, vectors, systems of equations, eigenvalues.
  • Statistics for Data Science 1 – Descriptive statistics, probability axioms, random variables, common distributions.
  • Statistics for Data Science 2 – Sampling distributions, estimation, hypothesis testing, confidence intervals.
  • English 1 & 2 – Communication skills, technical writing.
  • Python for Data Science – Python basics, data structures, NumPy, Pandas, data visualisation.

Diploma Level – Programming

  • Programming Concepts using Java – OOP, classes, inheritance, polymorphism, exception handling.
  • Data Structures and Algorithms using Java – Arrays, linked lists, stacks, queues, trees, graphs, sorting/searching.
  • Database Management Systems – ER model, SQL, normalisation, transactions.
  • Software Engineering – SDLC, requirement analysis, design, testing, project management.
  • Operating Systems & Computer Architecture – Processes, memory management, file systems, basic computer organisation.
  • Networking & Security – OSI/TCP/IP model, network security basics, cryptography.
  • Programming Project 1 & 2 – Team projects to build software applications.

Diploma Level – Data Science

  • Business Data Management – Data warehousing, ETL, data governance.
  • Machine Learning Foundations – Linear regression, logistic regression, k‑NN, decision trees, model evaluation.
  • Machine Learning Techniques – Ensemble methods, SVM, clustering, dimensionality reduction (PCA).
  • Data Visualization – Principles of visualisation, tools (Tableau, matplotlib, seaborn).
  • Tools for Data Science – Excel, R, SQL, cloud platforms (AWS/Azure basics).
  • Data Science Project 1 & 2 – End‑to‑end data analysis and modelling projects.

Degree Level (BSc & BS)

  • Artificial Intelligence – Search algorithms, knowledge representation, reasoning.
  • Deep Learning – Neural networks, backpropagation, CNNs, RNNs, training techniques.
  • Natural Language Processing – Tokenisation, embeddings, transformers, text classification.
  • Computer Vision – Image processing, feature extraction, object detection.
  • Reinforcement Learning – MDP, Q‑learning, policy gradients.
  • Generative AI – GANs, VAEs, LLMs, prompt engineering.
  • ML Engineering & MLOps – Model deployment, monitoring, CI/CD for ML.
  • Economics for Data Science – Basic micro/macroeconomics, econometrics.
  • Finance for Data Science – Financial markets, risk analysis, algorithmic trading.
  • Capstone Project – A substantial data science project integrating all prior learning.

More detailed syllabi are available on the IITM BSDS programme page.