- Individual Development Plan: https://myidp.sciencecareers.org/
- Interactive introduction to GIT: https://www.codecademy.com/learn/learn-git
- Gits manual: http://gitref.org/index.html
- Introductory course focusing on data scinece: https://www.datacamp.com/courses/intro-to-python-for-data-science
- Interactive introduction to python: http://learnpython.org/
- Introductory book on python 3: http://www.diveintopython3.net/
- Introduction to the scientific computing libraries: http://www.scipy-lectures.org/
- Code style guide: https://www.python.org/dev/peps/pep-0008/
- Google’s Python Style guide: https://google.github.io/styleguide/pyguide.html
- Tabular data API: http://pandas.pydata.org/
- Core graphing library: https://matplotlib.org/
- Matrix: http://www.numpy.org/
- Scientific computing (i.e., linear algebra): https://www.scipy.org/scipylib/index.html
- Machine learning: http://scikit-learn.org/stable/
- Data Visualization with R: http://blog.revolutionanalytics.com/2017/09/data-visualization-for-social-science.html
- http://derekwyatt.org/vim/tutorials/novice/
- http://derekwyatt.org/vim/tutorials/intermediate/
- http://derekwyatt.org/vim/tutorials/advanced/
- https://github.com/jfear/dotneovim
- https://stackoverflow.blog/2017/05/23/stack-overflow-helping-one-million-developers-exit-vim/
- http://www.openvim.com