MUSA-620-Fall-2019 / week-1

Exploratory Data Science in Python

Week 1
Exploratory Data Science in Python

Topics

• Course Logistics
• Using Jupyter Notebooks
• Introduction to Pandas

Initial Survey

Please fill out this anonymous survey for some initial feedback:

https://www.surveymonkey.com/r/BGK3S6Y

Recommended Readings

• 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.
• The official Anaconda User Guide for tutorials on using Anaconda and the installation guide for installing Anaconda.

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.

Python tutorials

There are 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/

Documentation

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

Additional Materials

• 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