• Course Logistics
• Using Jupyter Notebooks
• Introduction to Pandas

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• From Zero to Python
• Conda user guide
• Jupyter notebook documentation:
  • Introduction
  • Starting the notebook server
  • Creating a new document
  • Opening notebooks
  • Notebook user interface
  • Structure of a notebook document
  • User interface components
• Recommended tutorial for students with little Python background: Practical Python Programming

A number of resources are listed on the course website.

There are also a few good resources from the Berkeley Data Science Institute:

• https://bids.github.io/2016-01-14-berkeley/python/00-python-intro.html (notebook version)
• Python for Social Science, a free online book
• Many more resources are listed here: http://python.berkeley.edu/resources/

The The Python Data Science Handbook is a free, online textbook covering the Python basics needed in this course. In particular, the first four chapters are excellent:

• Chapter 1: IPython/Jupyter
• Chapter 2: Numpy
• Chapter 3: Pandas
• Chapter 4: matplotlib
• Note that you can click on the "Open in Colab" button for each chapter and run the examples interactively using Google Colab.

Particularly for the Data Science Handbook, there is too much to get through in one week, but the book should serve as your first reference on pandas, numpy, and matplotlib throughout the course.

• Setting up git
• Managing files on GitHub
• Managing files via the command line

• conda
• Jupyter notebook
• Guide to Markdown in notebook cells
• Binder Documentation
• pandas documentation
• matplotlib documentation

• StackOverflow: The Incredible Growth of Python
• 2015 Funding Proposal for the Jupyter Notebook
• A history of Jupyter Notebooks by its founder
• Google's Colab
• Tidy Data
• Joining Datasets
• ZIllow Data
• Introduction to writing your first Python program
• Visual Studio Code