29.1. Learning Python3
Python in a Nutshellfor a quick tour of Python.
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
Under Windows, if you have installed Git for Windows, you have access to
Git bashwhich 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
29.2.2. Resources to learn Git
To understand why you need to learn git, see Tools to do Reproducible Science
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
Modeling Psychophysical Data in R by Kenneth Knoblauch & Laurence T. Maloney
29.4. Stimulus/Experiment generation modules
29.5. Data analyses, Statistics in Python
Modules: numpy, scipy, pandas, seaborn, statsmodel, sklearn
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/)