https://github.com/tpoisot/ScientificComputingForTheRestOfUs

Introduction to Scientific Computing 🦊
https://github.com/tpoisot/ScientificComputingForTheRestOfUs

best-practices data-science educational-resources julia machine-learning reproducible-documents scientific-computing

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Introduction to Scientific Computing 🦊

• Host: GitHub
• URL: https://github.com/tpoisot/ScientificComputingForTheRestOfUs
• Owner: tpoisot
• License: cc-by-4.0
• Created: 2017-09-25T16:48:08.000Z (about 7 years ago)
• Default Branch: main
• Last Pushed: 2024-09-04T02:24:45.000Z (2 months ago)
• Last Synced: 2024-10-30T20:02:08.635Z (14 days ago)
• Topics: best-practices, data-science, educational-resources, julia, machine-learning, reproducible-documents, scientific-computing
• Language: Julia
• Homepage: http://sciencecomputing.org/
• Size: 66.8 MB
• Stars: 123
• Watchers: 11
• Forks: 13
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

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README

        
# Scientific Computing (for the rest of us)

**One specific challenge, when writing code as a scientist, is that we care *a

lot* about getting the *right* answer; but of course, the *right* answer is not

always obvious. So we should be very careful with the code we write. A piece of

code that crashes is annoying; but a piece of code that runs, and give you the

wrong answer can compromise your science and your career. This guide will help

you adopt practices that make it less likely to introduce mistakes in your code,

and more likely to catch them. Hopefully, this will let all of us write code we

can trust more.**

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Good principles in scientific computing can help you write code that is easier

to maintain, easier to reproduce, and easier to debug. But it can be difficult

to find an introduction to get you started. The goal of this project is to get

you started on the most important points. You can use these lessons on your own,

or as a group.

This material is aimed at people who have already interacted with a computer

using a programming language (we use *Julia*, but the code is meant to be fairly

general), but want to adopt best practices that make their code more robust. It

can also be used to facilitate the onboarding of new people in your lab or your

project.