Course schedule, 2019/2020
The (Rmarkdown) source code for these lectures is available at the github repository,
or by replacing the .slides.html suffix to .Rmd in the link below;
the slides are made using reveal.js.
Fall 2019
- Week 1 (10/1)
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Overview of data science - description and estimation, uncertainty and simulation, with examples for comparing means and linear regression; smoothing.
- Week 2 (10/7)
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Analysis of Variance (ANOVA) and experimental design; tidy data; power and false positives
- Reading: Quinn & Keough chapters 5, 7, 8
- Slides
- Week 3 (10/14)
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Plotting/visualization, and permutation/bootstrapping
- Slides
- Reading: Quinn & Keough chapter 9
- Week 4 (10/21)
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Multivariate ANOVA, regression, least-squares likelihood
- Slides
- Reading: Quinn & Keough chapter 6
- Week 5 (10/28)
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Model selection; random effects and mixed models - a first look.
- Slides
- Reading: Quinn & Keough chapter 13
- Week 6 (11/4)
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Bayesian statistics - prior distributions and uncertainty - using Stan)
- Slides
- Reading: Kruschke, chapters 1, 2, 4, 5, 6, 7
- Week 7 (11/11)
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Bayesian hierarchical modeling - shrinkage, and sharing power
- Slides
- Reading: Kruschke, chapters 9, 10 <!– - Homework 7 (due 11/21)
- Homework 7 Solution
- Homework 7 Solution Rmd File –>
- Week 8 (11/18)
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Logistic regression - simulation, posterior predictive sampling - robust regression
- Slides
- Reading: Kruschke, chapters 13, 21
- Week 9 (11/25)
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Robust multiple linear regression; scale mixtures; model selection with crossvalidation; introduction to Generalized Linear Models (GLM).
- Slides
- Reading: Kruschke, chapters 15, 16, 17
- Week 10 (12/2)
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More GLMs; Poisson regression; detecting and modeling overdispersion; quantifying goodness-of-fit.
- Slides
- Reading: Kruschke, chapter 24
Winter 2020
- Week 11 (1/7)
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Survival analysis
- Week 12 (1/14)
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Time series: temporal autocorrelation, autoregressive models; mechanistic models
- Week 13 (1/21)
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Categorical data: chi-square for contingency tables, permutation tests.
- Slides
- Reading: Kruschke, chapter 16 (metric data with one or two groups), and chapter 24 (Poisson, contingency tables)
- Week 14 (1/28)
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Categorical prediction; crossvalidation for model comparison.
- Slides
- Reading: Kruschke, chapter 22 (softmax regression)
- Week 15 (2/4)
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Many dimensions: Sparsifying priors and variable selection.
- Slides
- Reading: Kruschke, chapters 17 (one-variable linear regression), 18 (multivariate linear regression)
- Week 16 (2/11)
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Many response variables; data analysis example (cream cheese)
- Week 17 (2/18)
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Factor analysis, dimensionality reduction, and visualization; clustering; PCA, PCoA, MDS, t-SNE, UMAP
- Week 18 (2/25)
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Latent factors, deconvolution for mixtures of expression data; nonnegative matrix factorization
- Week 19 (3/3)
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Deconvolution continued; introduction to spatial statistics and kernel density estimation.
- Week 20 (3/10)
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Spatial statistics: spatial autocorrelation and Kriging.