29. Resources

29.1. Learning Python

• I strongly recommend the freely available book Automate the boring stuff with Python: Practical programming for total beginners. to total novices (as well as the other books by the author). For those who dislike reading, there are videos on the site.

• See Python in a Nutshell

• Books:

  • Automate the boring stuff with Python (highly recommended!)

  • Apprendre à Programmer avec Python3

  • Think Python

• MOOCs:

  • Udemy’s Python programming for absolute beginners

  • Code Academy’s Learn Python module

  • Openclassrooms’ Apprendre à programmer en Python

  • Python 3 : des fondamentaux aux concepts avancés du langage.

29.2. Programming skills

• Software Carpentry provides nice lessons about writing software for science and do reproducible science.

29.2.1. Resources to learn the command shell

• The Linux Command Line by Williams Shotts.

• Openclassrooms MOOC

Remarks:

• Under Windows, after having installed Git, you have access to git bash, which provides a terminal with the bash shell and emulates many unix commands.

• Under Windows 10, Microsoft has recently made available the “Windows Subsystem for Linux”, which provides a virtual Linux system running inside Windows. (See https://itsfoss.com/install-bash-on-windows/, and https://itsfoss.com/windows-linux-kernel-wsl-2/).

• Under MacOSX, when you open a terminal, you may be interacting withthe bash shell or the zsh shell (to know which, type echo $SHELL).

29.2.2. Resources to learn Git

To understand why you need to learn git, see Tools to do Reproducible Science

• Openclassrooms’ MOOC Manage your code with Git and Github

• https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners

• https://git-scm.com/book/en/v2/Getting-Started-Git-Basics

• The Git Book

• My own git cheat page

29.3. Books relevant to Cognitive and Brain Sciences Programming

• Programming Visual Illusions for Everyone by Marco Bertamini:

• Neural Data Science: A Primer with MATLAB and Python by von Erik Lee Nylen and Pascal Wallisch

• Matlab for Brain and Cognitive Scientists and Analyzing neural time series data by Mike X Cohen

• Python in Neuroscience

• Modeling Psychophysical Data in R by Kenneth Knoblauch & Laurence T. Maloney

29.4. Stimulus/Experiment generation modules

• http://www.expyriment.org (See Get started with Expyriment

• http://psychopy.org (See Programming with PsychoPy)

• http://psychtoolbox.org/ (See Psychtoolbox demos)

• https://www.jspsych.org/ (See intro at https://blog.s-m.ac/using-jspsych/)

• https://dialoguetoolkit.github.io/chattool/

29.5. Data analyses, Statistics in Python

• Modules: numpy, scipy, pandas, seaborn, statsmodel, sklearn

  • Data manipulation:

    • http://pandas.pydata.org/pandas-docs/stable/tutorials.html

  • Plotting:

    • http://matplotlib.org/users/pyplot_tutorial.html

    • https://seaborn.pydata.org/tutorial.html

• Scipy Lecture Notes: http://www.scipy-lectures.org/

• Think Stats by Allen B. Downey: http://greenteapress.com/thinkstats2/

• Python Data Science Handbook by Jake VanderPlas: https://jakevdp.github.io/PythonDataScienceHandbook

• Introduction to Data Science in Python: notebook from a 2 day workshop organized by the Paris-Saclay Center for Data Science: https://github.com/paris-saclay-cds/data-science-workshop-2019

• Machine Learning with scikit-learn” MOOC: https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/ (on github: https://inria.github.io/scikit-learn-mooc/)

29.6. Simulations

• Think Complexity by Allen B. Downey

• The Brian spiking neural network simulator

• Deep Learning for Natural Language Processing with Pytorch