There are SO many ways to learn now it can be overwhelming. Here are some suggestions from lab members about how to go about learning to code and get into data analysis. This page from the Pearson Lab at Duke has (an opinionated, but very) useful set of suggestions: https://pearsonlab.github.io/learning.html.

Online Courses#

• Software Carpentry and Data Carpentry have many short open-source courses with all the code and documentation available. They are short, well made, and to the point (also many are in Spanish). Here are some of note:
  • Plotting and Programming in Python a very nice introduction that highlights modern tools for data science and visualization.
  • Programming in MATLAB just a simple set of examples and quick start-up :)
  • Programming in R Basically the same scope as the MATLAB course but in R
  • The Unix Shell highly recommended if this has always seemed like a morass!
  • There are many other short courses on domain specific analysis too, have a look!
• CS50: Introduction to Computer Science, suggested by Jenny Lu and Helen Yang
• Lab in Cognition and Perception a really nice introduction to statistics and python for analysis in the guise of a cognitive science course
• Harvard’s online R courses, suggested by Rachel Wilson
• edX: Using python for research, suggested by Stephen Holtz
• coursera’s Machine Learning, suggested by Gu lab members

O’Reilly Learning#

Technical books are available through O’Reilly Learning Platform to which Harvard gives access. Of particular interest:

• Python Data Science Handbook
  • For understanding the Python data science ecosystem
  • The last edition was from 2016 but it (as of 2021) is still an incredibly useful and concise overview
• Fluent Python (2nd edition)
  • To help understand and write better Python code.
  • It has clear explanations of some of the patterns and syntax that will be foreign coming from MATLAB or R
• Think Julia (1st edition)
  • An extensive beginner programmer’s guide to using Julia, it says no previous experienced required but familiarity with MATLAB’s notation is probably quite helpful.
• NOTE: There are many MATLAB books as well, which appear very useful – please note here if you find one useful

LinkedIn Learning#

Harvard also gives access to LinkedIn Learning (formerly lynda.com) https://linkedinlearning.harvard.edu/. It has video courses for programming among other things (CAD software, etc.,). For example:

• learning to use GitHub, suggested by Alex Bates

NOTE: If anyone has used one and found it useful please add it below. Quick browsing showed these other offerings:

• Amazon Web Services (AWS) Essential Training https://www.lynda.com/Amazon-Web-Services-tutorials/AWS-Essential-Training-Developers/2817064-2.html
• Julia for Data Scientists https://www.lynda.com/Julia-tutorials/Julia-Data-Scientists-First-Look/512735-2.html

Others#

• learning Git branching, suggested by Stephen Holtz
• There is Only One [Statistical Hypothesis-Testing] Test, suggested by Stephen Holtz