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Course materials for General Assembly's Data Science course in San Francisco (11/30/15 - 3/2/16).

Instructor: Rob Hall

TA's:

• Justin Breucop
• Dave Yerrington

Installation and Setup Checklist

Git and Github Setup

Course Project Info

Course Project Examples

syllabus last updated: 12/12/2015

• Welcome from General Assembly staff
• Course overview (slides)
• Introduction to data science (slides)
• Command line & exercise (code)
• Exit tickets

Homework:

• Work through GA's friendly command line tutorial using Terminal (Linux/Mac) or Git Bash (Windows), and then browse through this command line reference.
• Watch videos 1 through 8 (21 minutes) of Introduction to Git and GitHub.
• If your laptop has any setup issues, please work with us to resolve them by Wednesday.

Resources:

• For a useful look at the different types of data scientists, read Analyzing the Analyzers (32 pages).
• For some thoughts on what it's like to be a data scientist, read these short posts from Win-Vector and Datascope Analytics.
• Quora has a data science topic FAQ with lots of interesting Q&A.

• Final project presentations from other class
• Q&A on course project expectations & schedule
  • public data sources
• Version Control with Git and GitHub (slides)
• Git Configuration and Github setup
• Moved to Class 3: Intro to Python (slides)
• Exit tickets

Homework:

• If you haven't already, complete the homework exercise listed in the command line introduction. Create a Markdown document that includes your answers and the code you used to arrive at those answers. Add this file to a GitHub repo that you'll use for all of your coursework, and submit a link to your repo using the homework submission form.

Git and Markdown Resources:

• Pro Git is an excellent book for learning Git. Read Chapter 1 - Getting Started and Chapter 2 - Git Basics to gain a deeper understanding of version control and basic commands.
• Very quick Git tutorial by Github and Codeschool. Recommended practice!
• Github's Mastering Markdown is a good starting point for learning github-flavored markdown.
• Markdown Cheatsheet provides a thorough set of Markdown examples with concise explanations.

Command Line Resources:

• If you want to go much deeper into the command line, Data Science at the Command Line is a great book. The companion website provides installation instructions for a "data science toolbox" (a virtual machine with many more command line tools), as well as a long reference guide to popular command line tools.

• Jupyter Notebook overview (slides)
• Intro to Python (slides)
• Linear algebra refresher (slides)

Python Resources:

• Codecademy's Python course: Good beginner material, including tons of in-browser exercises.
• DataQuest: Similar interface to Codecademy, but focused on teaching 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.

• Intro to Machine Learning (slides)
• Lab: KNN classification with Scikit-learn (notebook)

ML Resources:

• For a more formal, in-depth introduction to machine learning, read section 2.1 (14 pages) of Hastie and Tibshirani's excellent book, An Introduction to Statistical Learning. (It's a free PDF download!)

KNN Resources:

• A Detailed Introduction to KNN is a bit dense, but provides a more thorough introduction to KNN and its applications.
• Browse through the scikit-learn documentation for KNN to get a sense of how it's organized: user guide, module reference, class documentation

• Lab: numpy (notebook)
• Lab: pandas (notebook)
• Lab: Visualization with Bokeh (notebook)
• Model Evaluation, incl. Cross Validation (slides)
• Lab: Cross validation with Python and Scikit-learn (notebook)

Pandas Resources:

• To learn more Pandas, review this three-part tutorial.
• Browsing or searching the Pandas API Reference is an excellent way to locate a function even if you don't know its exact name.
• If you want to go really deep into Pandas (and NumPy), read the book Python for Data Analysis by Wes McKinney, the creator of Pandas. Ping me on Slack for a discount code.
• Here are examples of different types of joins in Pandas, for when you need to figure out how to merge two DataFrames.
• Optional: Read the Teaching Assistant Evaluation dataset into Pandas, create the X and y objects (the response variable is "class attribute"), and go through scikit-learn's 4-step modeling process. (There's no need to submit your code unless you have a question or would like feedback!)

Model Evaluation Resources

• For another explanation of training error versus testing error, the bias-variance tradeoff, and train/test split (also known as the "validation set approach"), watch Hastie and Tibshirani's video on estimating prediction error (12 minutes, starting at 2:34).
• Caltech's Learning From Data course includes a fantastic video on visualizing bias and variance (15 minutes).
• Random Test/Train Split is Not Always Enough explains why random train/test split may not be a suitable model evaluation procedure if your data has a significant time element.

Additional Resources:

• What I do when I get a new data set as told through tweets is a fun (yet enlightening) look at the process of exploratory data analysis.

• Regression: Linear, Multiple, Polynomial (slides)
• Regularization (slides)