Course materials for General Assembly's Data Science course in San Francisco, CA (11/2/16 - 1/25/17).
Lead Instructor: Nathaniel Tucker
Instructional Associate: Dan Bricarello
Course Producer: Vanessa Ohta
- Nate: Monday at 5pm
- Dan: Wednesdays at 4:30-6:30pm in the GA concourse
All will be held in the student center at GA, 225 Bush Street, SF.
Please fill this out at the end of each class!
- Install the Anaconda distribution of Python 2.7x.
- Install Git and create a GitHub account.
- Once you receive an email invitation from Slack, join our "GA Data Science 29 team" and add your photo! Dan will be on Slack during class and office hours to handle questions.
- Make sure you have everything installed as specified above in "Installation and Setup" by Monday
- Read this awesome intro to Git here
- Read this intro to the iPython notebook here
- Read Project 1 Instructions
- Go through the python files and finish any exercise you weren't able to in class
- Make sure you have all of the repos forked and ready to go
- Read Greg Reda's Intro to Pandas
- Take a look at Kaggle's Titanic competition
- Get started on Project 1, it's due next week!
- Project 1, it's due next week!
- Read through Project 2, it's due next week!
- Read through Project 2, it's due next week!
- Final Project, Deliverable 1 is due after thanks giving.
| Class | Date | Topic | Due |
|---|---|---|---|
| 1 | 11/2 | Intro to Data Science | |
| 2 | 11/7 | Intro to git/pandas | |
| 3 | 11/9 | Statistics Fundamentals I | |
| 4 | 11/14 | Statistics Fundamentals II | Unit Project 1 |
| 5 | 11/16 | APIs and Getting Data | |
| 6 | 11/21 | Linear Regression | |
| 7 | 11/28 | Linear Regression and Model Fit, Part 2 | Unit Project 2 |
| 8 | 11/30 | k-Nearest Neighbors | Final Project 1 |
| 9 | 12/5 | Logistic Regression | |
| 10 | 12/7 | Advanced Metrics and Communicating Results | |
| 11 | 12/12 | Decision Trees and Random Forests | Unit Project 3 |
| 12 | 12/14 | Natural Language Processing | Final Project 2 |
| 13 | 12/19 | Latent Variables and NLP | |
| 14 | 12/21 | Intro to Time Series Analysis | Final Project 2 |
| 15 | 1/4 | Time Series Modeling | Final Project 3 |
| 16 | 1/9 | Intro to Databases | |
| 17 | 1/11 | Advancing in Data Science | Unit Project 4 |
| 18 | 1/18 | Wrapping Up and Next Steps | Final Project 4 |
| 19 | 1/23 | Final Project Presentations | |
| 20 | 1/25 | Final Project Presentations, Part 2 | Final Project 5 |