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

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:

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

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.

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Comments

  1. Hello,

    I’m researching about statistics and design, and I came to this blogpost. I really liked it, but I wanted more information on this subject, specially the use of D3.js. Do you have the actual course recorded for iTunes U or any other online studying platform? ( Coursera, Edx, etc.?)

    Thank you very much,

    Cláudio

    Reply
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  4. Good looking stuff, I would like to download the presentations so I don’t have to be connected to the net to read them (and also so I can refer to them later). Are you purposefully preventing that or am I just not seeing how to do it?

    Reply
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