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