Education · 2023–27
IIT Madras
BS in Data Science and Applications, an online degree taken alongside IIT Bombay.
- ProgrammeBS, Data Science and Applications
- LevelDiploma
- ModeOnline, alongside IIT Bombay
Selected courses. All 19 are listed under Academics.
Data science and ML
Machine Learning Foundations
The maths under machine learning: least squares, eigendecomposition, SVD and PCA, convex and constrained optimisation, and estimation with EM.
Machine Learning Techniques
PCA and kernel PCA, clustering, mixture models, regression with regularisation, classifiers from k-NN to SVMs, and boosting.
Machine Learning Practice
The same methods in scikit-learn, end to end, from data preparation to tuned models.
Project: workspace rental price tier prediction
Introduction to Deep Learning and Generative AI
Neural networks, CNNs and sequence models, VAEs, GANs and diffusion models, and transformer language models, each with practice.
Project: industrial surface anomaly segmentation
Tools in Data Science
Sourcing, cleaning, analysing and visualising data, and deploying the results.
Programming
Programming, Data Structures and Algorithms using Python
Complexity, sorting and searching, graphs, union-find, heaps and search trees, greedy, divide and conquer, dynamic programming and network flows.
Database Management Systems
SQL, relational design and normal forms, storage, indexing, transactions, recovery and query optimisation.
Modern Application Development I
Web applications end to end: HTML and CSS, models, controllers, REST APIs, security, testing and deployment.
Project: examination management portal
Modern Application Development II
JavaScript and Vue front ends, state management, authentication, background jobs, messaging and performance.
Project: examination management portal, version 2
System Commands (Linux shell)
Working in GNU/Linux: packages, shell variables and scripts, redirection, AWK and make.
Mathematics and statistics
Mathematics for Data Science II
Linear algebra from vector spaces to orthogonality, multivariable calculus and optimisation.
Statistics for Data Science II
Multiple random variables, estimation, Bayesian inference and hypothesis testing.
Business
Business Analytics
Regression, logistic regression and time series applied to business decisions.
Business Data Management
Economics, accounting and financial statements, selling and negotiation, worked through Excel case studies.