Rohan Kannan

Courses

Selected coursework from my DS & Math double major at Purdue. The throughline: I want the theory underneath data science, not just the tooling.

ProbabilityPurdue · completedRandom variables, distributions, expectation, limit theorems.

The foundation everything else on this page builds on — distributions, conditioning, expectation, and the limit theorems that make statistics work.

Statistics in RPurdue · completedApplied statistical computing and inference in R.

Hands-on statistical inference in R — estimation, hypothesis testing, and regression on real datasets.

Data SciencePurdue · completedThe end-to-end workflow: wrangling, modeling, validation.

The complete pipeline from raw data to defensible conclusions — data wrangling, modeling, and validation.

Real AnalysisPurdue · completedRigor: limits, continuity, convergence, proof-writing.

Proof-based analysis — the course that teaches you what the calculus you learned actually says, and how to argue it precisely.

Linear AlgebraPurdue · completedVector spaces, eigenvalues, decompositions.

Vector spaces, linear maps, and eigendecompositions — the language most of machine learning is written in.

Stochastic CalculusSelf-studyBrownian motion, Itô calculus, SDEs — taught myself.

Self-taught: Brownian motion, Itô integration, and stochastic differential equations — the mathematics underneath derivatives pricing and much of quantitative finance.

Up next (2026–27)