Harvard Data Science

Thoughts on statistical computing and visualization

May 5, 2025

Harvard Stat 221 “Statistical Computing and Visualization”: all lectures online

by Sergiy Nesterko

Stat 221 is Statistical Computing and Visualization. It’s a graduate class on analyzing data without losing scientific rigor, and communicating your work. Topics span the full cycle of a data-driven project including project setup, design, implementation, and creating interactive user experiences to communicate ideas and results. We covered current theory and philosophy of building models for data, computational methods, and tools such as d3js, parallel computing with MPI, R.

All lecture slides are now available online:

I feel privileged to have been invited onto this journey together with the students. Together, we learned substantial theory, created interactive visualization, defined open problems in current research, structured our thinking about interactive user experiences, and are now finishing up working on course final projects with a roster of first-class course partners.

While the lectures are over, the journey of learning and new discoveries in data-driven projects doesn’t stop here. If anything, it’s only getting more interesting.

  1. Lecture 1 — Course Introduction
  2. Lecture 2 — Introduction to Visualization, Modeling, and Computing
  3. Lecture 3 — Intro VMC — Modeling and Computing
  4. Lecture 4 — Guest lecture by Rachel Schutt: Introduction to Data Science
  5. Lecture 5 — A More Rigorous Look at Visualization
  6. Lecture 6 — Statistical Models and Likelihood
  7. Lecture 7 — Likelihood Principle, MLE Foundations, Odyssey
  8. Lecture 8 — Stochastic Optimization for Inference, Odyssey
  9. Lecture 9 — Modeling with Missing Data and Latent Variables
  10. Lecture 10 — Expectation-Maximization Algorithm
  11. Lecture 11 — EM for HMMs, Properties of EM
  12. Lecture 12 — EM variants, Data Augmentation
  13. Lecture 13 — Likelihood + Prior = Posterior
  14. Lecture 14 — Missing Data and MCMC
  15. Lecture 15 — Hamiltonian Monte Carlo
  16. Lecture 16 — Decision Theory and Statistical Inference
  17. Lecture 17 — Parallel Statistical Computing
  18. Lecture 18 — Parallel Tempering
  19. Lecture 19 — Message Passing Interface for Parallel Tempering
  20. Lecture 20 — Equi-Energy MCMC Sampler
  21. Lecture 21 — Approximate Methods: Variational Inference
  22. Lecture 22 — Variational EM, Monte Carlo EM
  23. Lecture 23 — Hacker Level: Data Augmentation
  24. Lecture 24 — Interactive Experiences and Us
  25. Lecture 25 — The Final Lecture: Summing It Up

The offline save stopped at the homepage, so several critiques end where the original said “Continue reading.” Kudos: 136.