learning_resources.md

layout	title
page	Python Learning Resources

Asking questions

The best place to ask questions is probably Stack Overflow, but they like you to ask what they have decided are "good questions" there. Please take the time to read the "tour" for Stack Overflow, or this more in-depth essay on asking good questions by Eric Raymond - note that Eric DOES NOT want questions from anyone (unless he finds them in a forum)! Note also that the Berkeley Python community is pretty friendly, and you're always welcome to check with us on the mailing list.

Zero-installation intros

These resources include an integrated python interpreter that works through your web browser. To use the other resources, you'll need to get access to a python interpreter (described in the next section).

• Codecademy Python Track
• learnpython.org
• Code Wars

Intro materials on GitHub

• Dav's D-Lab materials
• Omoju's Hip Hop / Turtle curriculum

Getting Python

Simple consoles: PySchools and Codecademy Labs

The easy way, hosted IPython in your browser: Wakari.io

If you are using OS X or Linux (or similar), you likely already have Python installed. You can just run python from the command line. (If you don't know how to run things from the command line, it's probably easiest to start with the Wakari link above, so you only have one thing to learn! If you want more of a power-user experience then you can start with the Command Line Crash Course.)

If you install Python, you should follow the instructions on the IPython page. (IPython is an enhanced shell for Python.)

Other online tutorials

• Tutorial from the official Python website
• Other resources are available on the Python wiki
• A useful collection of python tutorials
• Introduction to Programming a set of mini-lecture notebooks

Video tutorials

• Coursera
• Khan Academy

Cheat Sheets

• A simple reference
• A dense, compact reference
• A long format reference

E-books

• Think Python: How to Think Like a Computer Scientist
• 6 Free ebooks for python
• Python for Data Analysis (a great intro to real scientific computing with Python)
• At Berkeley, we have access to the following e-book catalogs (which includes the Data Analysis book above):
  • O'Reilly Safari Bookshelf
  • Springer Link

Other topics

A general resource when you're exploring new topics is the Hitchhiker's Guide to Python. (Just like this site, you can contribute to the Hitchhiker's Guide via GitHub.)

Python for Data Analysis is a great place to get started. Or, you can learn about these tools from their websites:

• Pandas for reading / manipulating data
• Matplotlib (the most common graphing library, includes a great gallery)

And then look here for inspiration:

• View existing IPython notebooks
• A list of other interesting IPython notebooks

At Berkeley

UC Berkeley has a lot of great resources for learning scientific computing and data science. There are classes, tutorials, reading groups, institutes, and much more. Below are some things worth checking out:

Scientific computing resources at UC Berkeley

• The D-Lab
• The I-school masters in data science
• The Berkeley Institute for Data Science (more details soon)
• The Berkeley VC for research page on data science

Python Courses at UC Berkeley

• CS9H - Self-paced Python course. Requires some programming background. Good for people that know another language and want to pick up Python.
• AY250 - "Python computing for science." More advanced python course for scientists.

Intensives

Software carpentry offers a variety of trainings around the world, check their main site for bootcamp dates.