The (Rmarkdown) source code for these lectures is available at the github repository, or by replacing the .slides.html suffix with .Rmd in the link below; the slides are made using reveal.js. Here are the slides from Fall 2020/Winter 2021, Fall 2019/Winter 2020, and Fall 2018/Winter 2019.


Winter 2022

Week 11 (1/4)

Survival analysis

Week 12 (1/11)

Categorical data: chi-square for contingency tables, permutation tests.

Week 13 (1/18)

Time series models with Stan: mechanistic models

Week 14 (1/25)

Crossvalidation for model comparison; sparsifying priors and variable selection

Week 15 (2/1)

Many response variables

Week 16 (2/8)

Data analysis example

Week 17 (2/15)

Factor analysis, dimensionality reduction, and visualization; clustering; PCA, PCoA, MDS, t-SNE, UMAP

Week 18 (2/22)

Factor analysis contiued; latent factors, deconvolution for mixtures of expression data; nonnegative matrix factorization

Week 19 (3/1)

Introduction to spatial statistics and spatial autocorrelation; kernel density estimation.

Week 20 (3/8)

Spatial statistics: kernel density estimation and interpolation.

Fall 2021

Week 1 (9/29)

Overview of data science - description and estimation, uncertainty and simulation, with examples for comparing means and linear regression; smoothing.

Week 2 (10/6)

Analysis of Variance (ANOVA) and experimental design; tidy data; power and false positives

Week 3 (10/13)

Plotting/visualization, and permutation/bootstrapping

Week 4 (10/20)

Multivariate ANOVA, regression, least-squares likelihood

Week 5 (10/27)

Model selection; random effects and mixed models - a first look.

Week 6 (11/3)

Multiple testing, error rates, and some history.

Week 7 (11/10, no class Thursday)

Introduction to Bayesian statistics

Week 8 (11/17)

Bayesian hierarchical modeling - shrinkage, and sharing power

Week 9 (11/24, no class Thursday)

Logistic models, and sharing power

Week 10 (12/1)

Robust linear models; Generalized Linear Models (GLMs).