Conclusion¶

  • We've done a lot in 2 days (4 half days)!

  • Much more to learn, but you have the tools to keep going

  • The internet (Google, Stackoverflow) is your friend

Resources¶

  • The Python Data Science Handbook by Jake VanderPlas.
    • Approachable, broad, well-written
    • Available free online
  • Python for Data Analysis by Wes McKinney
    • Dense but extremely thorough
    • Probably the most comprehensive guide to Pandas
  • Hands-On Machine Learning by Aurélien Géron
    • Approachable but advances quickly
    • Most popular machine learning book for Python

Additional UC Python courses¶

  • Intermediate Python for Data Science
    • Learn to use control flow and custom functions to work with data more efficiently.
    • Build awareness and basic skills in working with Python from the shell and its environments.
    • Exposure to Python's data science ecosystem and modeling via scikit-learn.
  • Advanced Python for Data Science
    • Develop an intuition for the machine learning workflow and Python tooling.
    • Build familiarity with common software engineering tooling and methodologies for implementing a machine learning project.
    • Gain a high-level understanding of the function of data science-adjacent technologies that students will encounter in the workplace, focusing on Git and GitHub.

Thank You¶

  • We love being able to teach these workshops and we hope you enjoyed it as well!

  • We appreciate your feedback on the workshop. Constructive feedback allows us to continue to improve this course.