ESM 244

Winter 2019

Instructor: Allison Horst

Week	Lecture Links	Lab Materials
1	Lecture 1: Course intro + 206 review Lecture 2: Binary + ordinal logistic regression	Lab 1: data wrangling + visualization review
2	Lecture 3: Logistic regression, PCA intro Lecture 4: PCA/RDA continued	Lab 2: Ordinal logistic regression, PCA, Shiny example
3	Lecture 5: Bootstrapping Lecture 6: Exploring missingness	Lab 3: Bootstrap, assessing missingness
4	Lecture 7: Nonlinear least squares Lecture 8: Longitudinal data	Lab 4: NLS, panel regression example
5	Lecture 9: Exploring time series data Lecture 10: Autocorrelation, MA, ARIMA intro	Lab 5: Intro to time series analysis
6	Lecture 11: Spatial data, projections, variograms Lecture 12: Kriging	Lab 6: Getting, wrangling and viewing spatial data
7	Lecture 13: Point pattern analysis, k-means clustering	Lab 7: Rasters, Kriging, PPA
8	Lecture 14: Hierarchical clustering, Bayesian thinking intro Lecture 15: Bayesian + text analysis case studies	Lab 8: Cluster analysis (k-means + hierarchical), + some text wrangling/sentiment analysis
9	Lecture 16: Graph theory terms, data sci collaboration	Lab 9: Network analysis + cool extras