Pre-Course Learning Resources

Pre-Course Learning Resources

Pre-reading for our courses is not mandatory, but we recommend it as a highly effective learning strategy for getting the most benefit from the course. If you do some pre-reading, we encourage you to make a note of any questions that arise; then we can answer the questions in the course.

Resources for Beginners to Programming

  1. One good introduction is the book called "A Byte of Python". It is a good mix of theory and practice. The formatting is a little cumbersome. It is available free under a Creative Commons licence.

  2. “Learn Python the Hard Way”, by Zed Shaw, teaches elementary Python through exercises. It is practical but light on concepts and probably less useful as background reading than the book above. A free HTML version is available here.

  3. A book called "How to think like a computer scientist", by Alan Downey, uses Python to teach computer programming. It is not easy reading, but you would learn a lot. The first 13 chapters would give a beginning programmer an excellent level of background knowledge for this course.

  4. There is also a longer list of Python-learning links for beginners here.

Resources for Those with Some Experience with Programming

Pre-reading is not mandatory, but we recommend it as a highly effective learning strategy for getting the most benefit from the course. If you do some pre-reading, we encourage you to make a note of any questions that arise; then we can answer the questions in the course.

The pre-course reading that we recommend to our course participants who have programming experience in other languages is the first seven chapters of the official Python Tutorial. These are available online from here.

You will probably want to read the tutorial with an IPython interpreter (from e.g. Anaconda) handy to try out the examples as you see them. Reading through the entire first seven chapters this way takes about 4 hours.

Pre-Course Resources for the "Python for Scientists & Engineers" Course

If you would like to go deeper into some of the numerical and scientific computing packages we will use in the course, there is useful background reading material available here.

Pre-Course Resources for the "Python for Data Analysis" Course

There are also some useful introductory notes on the Pandas package for data analysis here.

A book is not necessary, but if you would like to read one before the course, we would recommend "Python for Data Analysis" by Wes McKinney, available at good bookstores everywhere.

Questions?

You are welcome to contact us if you have any questions before the course.
You can email us at: info@pythoncharmers.com

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