28.1. 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.
28.2. Programming skills¶
Software Carpentry provides nice lessons about writing software for science and do reproducible science.
28.2.1. Resources to learn the command shell¶
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
28.2.2. Resources to learn Git¶
To understand why you need to learn git, see Tools to do Reproducible Science
28.3. 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
28.4. Stimulus/Experiment generation modules¶
28.5. 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/)