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
