Note: Due dates of bigger assignments and exams will not move, however the timeline of topics might be updated throughout the semester.

Date Lesson Reading Assignment Slides Exam
PART 1: Visualizing data
Tue, Aug 29 Welcome to Data Science
Thur, Aug 31 Introduction to R/RStudio + git/GitHub
Tue, Sept 5 Fundamentals of data & data visualization
Thur, Sept 7 Visualizing spatial data, and more
Tue, Sept 12 Data Visualization and Exploration, Pt 1
Thur, Sept 14 Data Visualization and Exploration, Pt 2
Resolving merge conflicts
Tue, Sept 19 Confounding variables and Simpson's paradox
Thur, Sept 21 Case studies: SAT scores and smoking
PART 2: Wrangling data
Tue, Sept 26 Tidy data + data wrangling
Thur, Sept 28 Types of variables
Tue, Oct 3 Recoding variables and transformations
PART 3: Making rigorous conclusions
Thur, Oct 5 The language of models
Tue, Oct 10 No class: Fall break
Thur, Oct 12 Formalizing linear models
Tue, Oct 17 Multiple linear regression
Thur, Oct 19 Model selection
Case study: Model selection for Paris Paintings
Tue, Oct 24 Prediction and model validation
Case study: Model validation for course evals
Thur, Oct 26 Estimation via bootstrapping
Tue, Oct 31 Hypothesis testing via simulation methods
Thur, Nov 2 More simulation based inference
Tue, Nov 7 Inference overview
Thur, Nov 9 Inference for regression and Central Limit Theorem
Tue, Nov 14 CLT based inference
Case study: Inferring from the General Social Survey
PART 4 Looking forward
Thur, Nov 16 Web scraping
Tue, Nov 21 Functions and automation
Thur, Nov 23 No class: Thanksgiving
Tue, Nov 28 Interactive visualizations with Shiny, Pt 1
Thur, Nov 30 Interactive visualizations with Shiny, Pt 2
Tue, Dec 5 Bayesian inference
Thur, Dec 7 Final project presentations