.md .pdf

repository open issue suggest edit

Resources, Tutorials, and Data Sources

Note

Anything that is bolded/underlined below is also considered essential.

If you have any favorite resources you like, or found helpful, please let me know!

THE MOST ESSENTIAL RESOURCES

• Help: Google, Stack Overflow, Github help, JupyterLab documentation, Python help

• Cheat sheets to bookmark/print! Better yet, download these to your Notes repo, and put them in the “Codebook” folder therein!

  • Included in this folder: python basics, jupyter notebook, importing data, numpy, pandas, seaborn, and scikit-learn

• Coding best practices, and project management

Python

Note

If you use any of these and LIKE or DISLIKE them, please let me know so I can guide future students to resources.

• Essential: A whirlwind tour of python

• Essential: datacamp.com has many self guided lessons

• Lessons 3 - 5 of the official tutoral

• The official documentation

• Dive Into Python

• www.pythoncheatsheet.org

• The best compilation of coding resources on the web, including:

  • free python books,

  • free interactive tutorials,

  • free courses

  • cheatsheets

Data Science

• Essential: Python Data Science Handbook

• Probably the best collection of Python notebooks on the web

Visualization

• Essential: Kaggle’s Data viz tutorial is excellent. It has reproducible code and data, using python.

• Essential: An Economist’s Guide to Visualizing Data is excellent as well.

• Essential: Data Visualization: A practical introduction, by Kieran Healy especially discusses the “whys” of visualization in a smart way. The walkthroughs are in R, not python, however.

• STAT545 on good visualization

• Some good data viz blogs

Github, Git, and Version control

• Getting started on GitHub and a twitter length description of how a project flows

• Jenny Bryan on version control

• Again… Coding best practices, and project management

• The most thorough yet simple walkthrough of Git and Github use on the web. Applies to python use for the most part.

Data/ML

• Scikit (python package) can read in some data, which has data on Boston real estate, wine, a larger california housing dataset

• Essential: Pandas can read in a LOT of useful data! Data providers include: Federal Reserve (“FRED”), Ken French, NASDAQ, OECD, Qunadl, TSP, World Bank, and more!

• ML competitions with serious prizes at drivendata.org

  • This comp was interesting. You could start trying to analyze it here. This has a good example of the process you might follow. After you’re done, you can see the winner’s code and discussion of the winning approach

• Essential: kaggle.com has ML competitions, some FAQs, tutorials, data and competitions

  • Real estate data, a tutorial exploring that data, and a pass at a model

  • Philly based data would be fun. Here is real estate, one option for data, seems ok, N=805

  • Predict box office for movies. VaultML claims they can do this by reading the screenplays and using textual analysis tools

  • Wine, but not necessarily the best data source

• UC Irvine has a data repo, some of these are available via scikit package

  • Predicting where the wine is from (wine/location <— easy starter challenge (where is the wine from?)

  • Wine Quality

  • German credit data by person

• More good sources: data.gov, data.census.gov, data.world, https://ourworldindata.org is incredible and also has many repos on Github including one that imports data via python, …

Books

• Signal and the Noise, by Nate Silver

• Range, by David Epstein is a very interesting book generally, and it touches on prediction skill too

• Superforecasters. Here is a decent free summary

Need help? Tips and hacks