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During the end of my studies, from 2018 to 2020 I partook in setting up lecture content, assignments and atomically verifying assignments for the course Scientific Programming in Python. Me and another student together fully planned and implemeented this lecture series from scratch.
In it, we start with basic and advanced Python, as well as tools such as git, Jupyterlab and the Debugger, to subsecquently go over the libraries necessary for data handling (Numpy, Pandas), visualization (Matplotlib, Plotnine, Seaborn, Altair), analysis (Statsmodels, Scipy) and for experiment creation (Expyriment, Psychopy), to teach the full circle of working with data scientifically. The course was implemented as an interactive lecture (using Jupyter Notebooks) with weekly homework which are automatically graded using GitHub Classroom together with custom tools and a custom dashboard for the students, followed by an optional open-book-exam. All lecture material (including recordings) is open-source and available on GitHub/Youtube. For a course syllabus, full lecture material, dashboard demo, evaluations as well as recordings of all iterations it is referred to the landing page. The course content overview can be found here. Held for three iterations with 100-130 students per iteration. 2020’s iteration held completely online due to the COVID-19-pandemic.