Lecture 1: What is EDA?
[R code] [data]
Lecture 2: Univariate data visualization
[R code] [Reading: Cleveland pp. 16-22, 24-25, 29-33 and ggplot2 book Sections 2.1-2.8]
Lecture 3: Comparing univariate distributions and tidy data
[R code] [Reading: Tidy data section of R for Data Science]
Lecture 4: Computational tools and transformations
[R code] [Reading: R for data science chapters 10 and 12, Cleveland pp. 42–67] [data]
Lecture 5: Building Simple Models
[R code] [Reading: Cleveland pp. 34-41, 68-79]
Lecture 6: ggplot, more formally
[R code] [Reading: ggplot paper] [data 1, data 2, data 3]
Lecture 7: Bivariate data
[R code] [Reading 1, Reading 2] [data]
Lecture 8: Flexible modeling for bivariate data
[R code] Reading: Cleveland pp. 91-110
Lecture 9: Smoothing part II
[R code] [Recording]
Lecture 10: Robust regression and comparing bivariate datasets
[R code]
Lecture 11: Interactions and coplots for trivariate data
[R code] Reading: Cleveland pp. 184-190, 194-199, 204-205
Lecture 12: More interactions, coplots, and modeling
[R code]
Lecture 13: Level and contour plots
[R code] Reading: Cleveland pp. 228-241, 245-248, 257-266, 270
Lecture 14: Model-building with a moderate number of variables
[R code] [data]
Lecture 15: Logistic regression
[R code] [data]
Lecture 16: Logistic regression 2
[R code] [data]
Lecture 17: Count responses and Poisson regression
[R code]
Lecture 18: Categorical data and contingency tables
[R code] Reading: vcd tutorial
Lecture 19: Ordered and unordered categorical responses
[R code] [data 1, data 2]
Lecture 20: EDA and the problem of multiple comparisons
[R code] Reading: Gelman and Loken, “The garden of forking paths”
Lecture 21: Background for hypervariate data and biplots
[R code] Reading: Greenacre chapter 1
Lecture 22: Singular value decomposition and reduced-rank biplots
[R code] Reading: Greenacre chapter 5
Lecture 23: Principal components analysis
[R code] Reading: Greenacre chapter 6
Lecture 24: Scree plots and choosing the number of dimensions
[R code] [data 1, data 2, data 3]
Lecture 25: Multi-dimensional scaling
[R code] [data]
Lecture 26: Linear discriminant analysis
[R code]