An introduction to Python for non-programmers using inflammation data.
This lesson teaches novice programmers to write modular code to perform data analysis using Python. The emphasis, however, is on teaching language-agnostic principles of programming such as automation with loops and encapsulation with functions, see Best Practices for Scientific Computing and Good enough practices in scientific computing to learn more.
The example used in this lesson analyses a set of 12 files with simulated inflammation data collected from a trial for a new treatment for arthritis. Learners are shown how it is better to automate analysis using functions instead of repeating analysis steps manually.
The rendered version of the lesson is available at: https://swcarpentry.github.io/python-novice-inflammation/
This lesson is also available in R and MATLAB.
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes!
Lesson maintainers are Trevor Bekolay, Maxim Belkin, Anne Fouilloux, Valentina Staneva, Mike Trizna, and creator of Software Carpentry: Greg Wilson
A list of contributors to the lesson can be found in AUTHORS
To cite this lesson, please consult with CITATION
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