Workshop at General Assembly (Washington, DC) on October 20, 2014.

Instructor: Kevin Markham

1. Why Python? (10 min.)
   • Characteristics of Python
   • Python vs. R
   • Why Anaconda?
2. Just Enough Python Basics (45 min.)
   • Python interpreter (aka "Python shell"), IPython shell
   • Running a simple script (code)
   • Spyder IDE
   • Exploring data types, lists, functions (code)
3. Getting Data (20 min.)
   • Public datasets in structured formats
   • Accessing APIs (code, documentation)
   • Scraping websites (code, pages)
4. Looking at Data (5 min.)
   • Data from FiveThirtyEight (GitHub repository)
   • Alcohol consumption (article, modified data)
5. Pandas for Data Exploration (70 min.)
   • Exploring alcohol data: examining, summarizing, filtering, sorting, handling missing values, split-apply-combine (code)
   • If time permits, also explore movie ratings data (description): joins, plotting
6. Brief Tour of Other Modules for Data Science (5 min.)
   • numpy
   • scikit-learn
   • statsmodels
7. Recommended Resources for Self-Learning (10 min.)
   • Basic Python: Codecademy, Google's Python Class, Python Tutor (to visualize code execution)
   • Pandas: tutorial, book: "Python for Data Analysis" (includes numpy and basic Python)
   • Web scraping: tutorial
   • Command line: tutorial
   • Git and GitHub: video series
   • Machine learning: book and videos: "An Introduction to Statistical Learning", scikit-learn tutorials, Data Science as a Sport (video), Kaggle Titanic competition
   • Data science in general: ebook: "Analyzing the Analyzers"
   • Data-focused newsletters: Center for Data Innovation, O'Reilly Data Newsletter, Data Community DC
   • Full-fledged courses: Data Science Specialization (9 short courses by JHU in R), Machine Learning (1 course by Andrew Ng in Matlab/Octave), Learning from Data (1 course, programming language not specified)
8. General Assembly's Data Science Course (5 min.)
   • Official page
   • Current repo
   • Project examples
9. Ask Me Anything