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Episode #252: What scientific computing can learn from CS

Published Fri, Feb 21, 2020, recorded Thu, Jan 30, 2020.

This episode is carbon neutral.
Did you come into Python from a computational science side of things? Were you just looking for something better than Excel or Matlab and got pulled in by all the Python has to offer?

That's great! But following that path often means some of the more formal practices from software development weren't part of the journey.

On this episode, you'll meet Martin Héroux, who does data science in the context of academic research. He's here to share his best practices and lessons for data scientists of all sorts.

Links from the show

Neuroscience Research Australia: neura.edu.au
Martin Héroux: researchgate.net

Errors in science: I make them do you? Part 3: scientificallysound.org

PyPI Packages
DABEST: pypi.org/project/dabest
PSYCHOPY: pypi.org/project/PsychoPy

Spreadsheet Blunders
12 of the Biggest Spreadsheet Fails: blogs.oracle.com
Common spreadsheet errors: datacarpentry.org

Best Practices for Scientific Computing: journals.plos.org
Good enough practices in scientific computing: journals.plos.org
Full episode RSS feed: talkpython.fm/episodes/rss_full_history

Springboard bootcamp scholarships [code TALKPYTHONTOME]: talkpython.fm/springboard

Martin Héroux
Martin Héroux
Episode sponsored by
Ads served ethically
Martin Héroux is a neuroscientist who works at
Neuroscience Research Australia with a passion for quality, reproducible
research.
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