Schedule
| Week | Link | Topic | Lab | Notes |
|---|---|---|---|---|
| 1 | Lec 1 | Introduction | No Lab | CIT Ch. 1 |
| 2 | Lec 2 Lec 3 Lec 4 |
Plots and Hypotheses | Lab 0: Setup | CIT Ch. 2 |
| 3 | Lec 5 | Tabular Data and Python | Lab 1: Using Pandas | No Class M (Labor Day) DS CH. 1, 2 |
| 4 | Lec 8 | Statistics Intro | Lab 2: Visualizing Stats | |
| 5 | Lec 9 Lec 10 Lec 11 |
Data Wrangling | Lab 3: Filtering and Aggregation | DS Ch. 3 |
| 6 | Lec 12 Lec 13 Lec 14 |
Data Limitations | Lab 4: Dirty Data | |
| 7 | Lec 15 | Visualizations I | Lab 5: Plot Critique | No Class F (Fall Break) |
| 8 | Visualizations II | Project Workshop | ||
| 9 | Communicating Findings | Lab 6: Filing a Report | Project Exploration Due F @ 11:59pm | |
| 10 | Inferential Statistics | Lab 7: A/B Tests | Guest Lecture: Daniel Berry, Data Science Mangager @ Mozilla Firefox | |
| 11 | Linear Relationships | Lab 8: Linear Regression | Project Analysis Due F @ 11:59pm | |
| 12 | Uncertainty | Lab 9: Communicating Confidence | ||
| 13 | Private Data Analysis | Lab 10: Private Data Analysis | ||
| 14 | Causal Inference | No Lab | No Class W/F (Thanksgiving Break) | |
| 15 | Project Presentations | No Lab | Project Report Due F @ 11:59pm |
Assigned reading is from Computational and Inferential Thinking: The Foundations of Data Science (CIT) and Data Science: A First Introduction with Python (DS).