Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).
Instructor: Kevin Markham
| Tuesday | Thursday |
|---|---|
| 8/18: Introduction to Data Science | 8/20: Command Line and Version Control |
| 8/25: Data Reading and Cleaning | 8/27: Exploratory Data Analysis |
| 9/1: Visualization Project Discussion Deadline |
9/3: Machine Learning Project Question and Dataset Due |
| 9/8: Getting Data | 9/10: K-Nearest Neighbors |
| 9/15: Basic Model Evaluation | 9/17: Linear Regression |
| 9/22: First Project Presentation | 9/24: Logistic Regression |
| 9/29: Advanced Model Evaluation | 10/1: Naive Bayes and Text Data |
| 10/6: Natural Language Processing | 10/8: Kaggle Competition, Draft Paper Due |
| 10/13: Decision Trees | 10/15: Ensembling |
| 10/20: Regularization and Clustering, Peer Review Due |
10/22: Course Review |
| 10/27: Final Project Presentation | 10/29: Final Project Presentation |
- Install Git.
- Create an account on the GitHub website.
- It is not necessary to download "GitHub for Windows" or "GitHub for Mac"
- Install the Anaconda distribution of Python 2.7x.
- If you choose not to use Anaconda, here is a list of the Python packages you will need to install during the course.
- We would like to check the setup of your laptop before the course begins:
- You can have your laptop checked before the intermediate Python workshop on Tuesday 8/11 (5:30pm-6:30pm), at the 15th & K Starbucks on Saturday 8/15 (1pm-3pm), or before class on Tuesday 8/18 (5:30pm-6:30pm).
- Alternatively, you can walk through the setup checklist yourself.
- Once you receive an email invitation from Slack, join our "DAT8 team" and add your photo.
- Practice Python using the resources below.
- Codecademy's Python course: Good beginner material, including tons of in-browser exercises.
- DataQuest: Teaches Python in the context of data science.
- Google's Python Class: Slightly more advanced, including hours of useful lecture videos and downloadable exercises (with solutions).
- A Crash Course in Python for Scientists: Read through the Overview section for a quick introduction to Python.
- Python for Informatics: A very beginner-oriented book, with associated slides and videos.
- Python Quick Reference Guide: My beginner-oriented guide that demonstrates Python concepts through short, well-commented examples.
- Python Tutor: Allows you to visualize the execution of Python code.