In this course, we'll use the following technology stack for numerical computations

• Git (version control)
• Docker (containers) only if you decide to work with it.
• Python + virtualenv / Anaconda
• jupyter (colab ?) notebooks
• numpy + scipy (matrices)
• scikit-learn (ml algorithms + pipelines)
• matplotlib (plotting)
• pandas (low scale data processing)
• probably? tensorflow. (symbolic computation engine)

A simple roadmap to installing them can be found here -

• GIT
• Docker, Docker tutorial
• Python, Python tutorial + virtualenv, Anaconda, Anaconda tutorial, virtualenv tutorial
• numpy, scipy, scikit-learn, matplotlib, pandas are already included in anaconda distribution, you can install them separately with pip (see python + virtualenv tutorials)
• tensorflow

The frameworks can be easily installed on Mac OS and Linux. Windows installation is, a bit tougher, so if you don't feel like it, try using docker (e.g. kitematic gui or console on windows).

If you so want, you can use any other framework for your projects, but we can't guarantee we'll be able to support it.

Any issues concerning installation can just as well be sent to this thread.