List of quick tutorials for scientific computing

Learn to use Markdown to make 'modern' text files (10 minutes):

• https://commonmark.org/help/tutorial/

'Everything' you need to know about the 'command line' and writing shell scripts (~1 hour each):

• http://linuxcommand.org/lc3_learning_the_shell.php
• http://linuxcommand.org/lc3_writing_shell_scripts.php
• Both of the above as a book/pdf: http://linuxcommand.org/tlcl.php

A good introduction to pandas (think of this as an alternative to excel):

• https://bitbucket.org/hrojas/learn-pandas
• (Many Python books, take your pick) https://github.com/EbookFoundation/free-programming-books/blob/master/free-programming-books.md#python

A good introduction to R (think of this as an alternative to SPSS/STATA):

• https://psyteachr.github.io/msc-data-skills/
• https://www.edx.org/course/introduction-to-r-for-data-science-2

And now Jupyter Notebooks, a great way to organize your python, matlab, and R code:

• https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook

Then add Git, the modern way to keep track of versions of your code and share files as well:

• https://guides.github.com/

Good Articles to Read:

• https://software-carpentry.org/lessons/index.html
• http://swcarpentry.github.io/good-enough-practices-in-scientific-computing/
  • (after Best Practices article): https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745
• https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004947
• http://swcarpentry.github.io/git-novice/
• https://guides.github.com/introduction/flow/
• https://hbctraining.github.io/Training-modules/
• https://realpython.com/documenting-python-code/
• http://statsthinking21.org/