DAT8 – General Assembly’s Data Science course in Washington, DC
DAT8 Course Repository
Course materials for General Assembly’s Data Science course in Washington, DC (8/18/15 – 10/29/15).
|8/18: Introduction to Data Science||8/20: Command Line, Version Control|
|8/25: Data Reading and Cleaning||8/27: Exploratory Data Analysis|
|9/1: Visualization||9/3: Machine Learning|
|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|
|10/13: Decision Trees||10/15: Ensembling|
|10/20: Advanced scikit-learn, Clustering||10/22: Regularization, Regex|
|10/27: Course Review||10/29: Final Project Presentation|
- Codecademy’s Python course: Good beginner material, including tons of in-browser exercises.
- Dataquest: Uses interactive exercises to teach 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).
- Introduction to Python: A series of IPython notebooks that do a great job explaining core Python concepts and data structures.
- Python for Informatics: A very beginner-oriented book, with associated slides and videos.
- A Crash Course in Python for Scientists: Read through the Overview section for a very quick introduction to Python.
- Python 2.7 Quick Reference: My beginner-oriented guide that demonstrates Python concepts through short, well-commented examples.
- Beginner and intermediate workshop code: Useful for review and reference.
- Python Tutor: Allows you to visualize the execution of Python code.