Introduction to Data Science course handbook

When we were designing our Data Science Master’s program we did so from the ground up. The courses are not standalone but interconnected. This way we know what has already been covered, so we can cover more content and in ways that would otherwise not be possible. It also means that we have to put in a lot of extra work to produce custom materials. As the materials mature, we want to share them with everyone, so that anyone can benefit or contribute.

The first such text is the handbook that supports the Introduction to data science course. This intensive course is their starting point - a 1st semester course that introduces the students to the general standards (reproducibility, code versioning…), methods (visualization, predictive modeling…), and tools (Python, Docker, Git…) that we expect them to learn and apply throughout their studies and careers.

The latest version of the handbook is available here.

And the complete source is available on GitHub.

If you think some important aspect is not covered by the handbook or have any other comments or suggestions on how to improve it, please let us know, annotate the handbook, or raise an issue on GitHub!

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