SF DS 37 Course Repository
Course materials for General Assembly's Data Science course in San Francisco
Team
Lead Instructor: Nathaniel Tucker
Office hours
- Nate: Monday at 5pm
All will be held in the student center at GA, 225 Bush Street, SF.
Exit Tickets
Please fill this out at the end of each class!
Unit Projects
Final Project
Tentative Schedule (dates are fixed, topics will likely change)
| 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 |