Course materials for General Assembly's Data Science course in San Francisco
Lead Instructor: Nathaniel Tucker
- Nate: Monday at 5pm
All will be held in the student center at GA, 225 Bush Street, SF.
Please fill this out at the end of each class!
| Class | Topic | Due |
|---|---|---|
| 1 | Intro to Data Science | |
| 2 | Intro to git/pandas | |
| 3 | Statistics Fundamentals I | |
| 4 | Statistics Fundamentals II | Unit Project 1 |
| 5 | APIs and Getting Data | |
| 6 | Linear Regression | |
| 7 | Linear Regression and Model Fit, Part 2 | Unit Project 2 |
| 8 | k-Nearest Neighbors | Final Project 1 |
| 9 | Logistic Regression | |
| 10 | Advanced Metrics and Communicating Results | |
| 11 | Decision Trees and Random Forests | Unit Project 3 |
| 12 | Natural Language Processing | Final Project 2 |
| 13 | Latent Variables and NLP | |
| 14 | Intro to Time Series Analysis | Final Project 2 |
| 15 | Time Series Modeling | Final Project 3 |
| 16 | Intro to Databases | |
| 17 | Advancing in Data Science | Unit Project 4 |
| 18 | Wrapping Up and Next Steps | Final Project 4 |
| 19 | Final Project Presentations | |
| 20 | Final Project Presentations, Part 2 | Final Project 5 |