One must learn how to code to be an effective cognitive neuroscience researcher. Our lab uses Python and Bash scripting for data analyses and statistics. Below are some recommended resources to get started on these languages.

  • Learn Python The Hard Way is a great website for learning Python's syntax. Codecademy is also great for beginners.
  • Here is a Jupyter notebook with a great collection of scipy related links.
  • Check out the Data Science Handbook by Jake Vanerplas to learn Python packages for scientific computing (e.g., numpy, pandas, sci-kit learn).
  • Allmost all softwares developed for neuroimaging only run on Linux or MacOS, it is essential that you learn BASH command line for scripting. Also check out this more advanced tutorial.
  • Use Seaborn to make figures.
  • Learn Regular expression.
  • Atom is a nice, free text editor.
  • Statistics

    Some links on stats:

  • Mumford Brain Stats, for fMRI data analysis.
  • Read this great paper on how to do non-parametric statistics on neuroimaging data. Though written primarily for electrophysiology data, the principle is applicable to other types of neuroimaging data or related metrics. In my experience, non-parametric approach requires less asumption, thus more flexible. Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.
  • Here is a very accessible tutorial explaining the baisc logic behind bootstraping and resampling statistics.

    Here is a list for students working with me interested in learning more about fMRI:

  • How to learn fMRI by Jonathan Peelle, I agree with him 100%.
  • Coursera course on fMRI, part 1
  • Coursera course on fMRI, part 2
  • AFNI bootcamp slides. I'm primarily a AFNI user.
  • A nice textbook: Huettel, Scott A., Allen W. Song, and Gregory McCarthy. Functional magnetic resonance imaging. Sunderland: Sinauer Associates.

  • We use MNE for EEG/MEG analysis.