Materials for 1-day workshop DS3 Introduction to Python workshop

• ./data - Data files used in the workshop
• ./exercises - Jupyter Notebooks with class exercises
• ./lectures - Lecture materials (as Jupyter Notebooks and compiled PDF/HTML files)
• ./syllabus - Copy of workshop syllabus

• For this workshop I recommend using one of the 2 online platforms for working with Jupyter Noteboks:
  • Google Colab, a cloud platform for hosting Jupyter Notebooks. You need to have a Google account, but it does not require any local installations.
  • Kaggle Code, a platform for sharing and exploring data-science-focussed Jupyter Notebooks. Although technically owned by Google, you can register just for Kaggle website.
• If you would prefer to install Jupyter Notebook on your local machine, there are two main ways to do this: pip and conda. Unless you have prior experience with Python, I recommend installing Anaconda distribution, which contains all the packages required for this course.

There are many great online resources and published books on programming in Python. Some of them also provide a good coverage of using Python for data analysis. Here are some pointers to start from:

Books:

• Guttag, John. 2021 Introduction to Computation and Programming Using Python: With Application to Computational Modeling and Understanding Data. 3rd ed. Cambridge, MA: The MIT Press

• McKinney, Wes. 2017. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd ed. Sebastopol, CA: O'Reilly Media

• Sweigart, Al. 2019. Automate the Boring Stuff with Python. 2nd ed. San Francisco, CA: No Starch Press

Online:

• Python For You and Me

• Python Wikibook

• Python 3 Documentation (intermediate and advanced)

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