Resources

Subpages

• Notebook Checklist
• Glossary
• Common Problems
• Notes on Probability
• Git Resources
• Tutorials

Software

• Anaconda Python (install this in your Onyx home directory for use there)

To complete assignments on department computers, you also need:

• An SSH client (MobaXterm for Windows, ssh on Mac or Linux)
• The Boise State VPN for convenient access to Onyx nodes

For building more advanced workflows, there are many text editors you can use. I use Visual Studio Code, which has very good remote editing support.

Documentation

• Python
• Pandas
• Seaborn
• Scikit-Learn

Visualization

• Seaborn gallery
• Seaborn tutorial — organized topically, very good resource
• Matplotlib gallery
• Plotnine gallery
• My plot utilities (for preparing papers with plotnine)

Data Sets

Quick links to some of the data we use in this class:

• Palmer Penguins (local CSV version)
• HETREC Movie Data
• World Bank Indicators
• US Census
• CHI Papers

Infrastructure Help

• Piazza Student Support

Books

• Python for Data Analysis, 2nd Edition by Wes McKinney (O’Reilly, ISBN 978-1491957660). Primary textbook.

  You can read this book for free in two ways:

  • Through the Boise State Library
  • If you are an ACM member, through the ACM's O'Reilly Learning Platform subscription (direct link)

• NIST/SEMATECH e-Handbook of Statistical Methods.

• Think Like a Data Scientist by Brian Godsey (Manning, ISBN 978-1633430273). Supplementary textbook.
• Learn Python the Hard Way by Zed Shaw. More thorough treatment of Python.
• Fluent Python. Learn advanced and idiomatic Python.
• Data Feminism by Catherine D'Ignazio and Lauren F. Klein. Critical perspectives on data.
• W.E.B. Du Bois's Data Portraits: Visualizing Black America, edited by Whitney Battle-Baptiste. Historical data visualizations.