Konrad Völkel
Data Science (DS) is a compulsory module in the computer science bachelor's programme at HHU Düsseldorf: probability theory and machine learning, worked through hands-on in Python with Numpy, Pandas and Scikit-learn throughout. It builds upon the Programming module and the three Mathematics for Computer Science modules.
The course is taught entirely in German: lectures, exercise class, slides, exercise sheets, sample solutions, recordings and the lecture notes. This page and the module description below are translations.
Data Science has run every summer term since 2022, taught by Konrad Völkel; it is not running this term. The next run is set for summer 2027, with lectures held by Prof. Dr. Milica Gašić and the exercise class by Konrad Völkel.
What the course covers
- Probability spaces, random variables, discrete and continuous distributions
- Expectation, variance, independence, the law of large numbers
- Entropy, Bayes' theorem, likelihood
- Exploratory data analysis with Numpy, SciPy and Pandas
- Descriptive statistics, scales of measurement, data cleaning
- Multivariate distributions, covariance, correlation
- Data protection, as a glossary of the legal terms
- Clustering with k-means, dimensionality reduction, principal component analysis
- Linear and kernel regression, statistical tests, model selection
- Estimators, maximum likelihood, expectation maximization
Recorded lectures, freely accessible
The whole run of summer 2025 is public on HHU's media server — twenty-four recordings, about 90 minutes each; the numbering starts at VL 0, and VL 1 is missing — in German, no login needed. Together with the lecture notes below they are the openly accessible part of the course; slides, exercise sheets and sample solutions live in the ILIAS course behind an HHU login. Whether future runs get recorded as well is not settled.
Lecture channel "Data Science" in the HHU Mediathek — twenty-four recordings, about 90 minutes each.
Material
The lecture notes, written for this course and freely readable, in German:
- Data Science (PDF) — there is some extra background content in the lecture notes.
The two main sources behind the lecture notes:
- Georgii, Stochastik: Einführung in die Wahrscheinlichkeitstheorie und Statistik (De Gruyter, 5th edition 2015). The source for the probability theory. In German.
- Grus, Data Science from Scratch: First Principles with Python (O'Reilly UK, 2nd edition 2019). The source for the Python part. In English.
Other books recommended alongside it, with a different style and more material:
- VanderPlas, Python Data Science Handbook (O'Reilly, 2016). For readers who would rather read a book than package documentation for Numpy, Pandas and Matplotlib; the author publishes it for free. In English.
- Deisenroth, Faisal and Ong, Mathematics for Machine Learning (Cambridge University Press, 2020). More of the linear algebra behind the course. In English.
Using this material
The lecture notes are published under CC BY-SA 4.0: the rendered pages and the PDF are linked above. If you teach something in this area and want the rest — the slides, the exercise sheets, the sample solutions, the Jupyter notebooks or the MyST sources of the notes — write to me and I will send them. All of it is in German.
The official module description
This is what the module is formally examined against, and what an examination office needs if the course is to count for something elsewhere. It is the entry in the Modulhandbuch B.Sc. Informatik PO 2021 (version of 22 July 2026, page 12), freely translated from the German original:
Contents. Data science is the application of statistical methods and methods of machine learning to data of any kind, by computer, to model systems and predict behaviour: probability theory — discrete and continuous distributions, Bayes' theorem, independence, the normal distribution, multivariate distributions, transformations; machine learning — data and models, estimation theory, classification, regression, dimensionality reduction, cluster analysis; and Python packages for data science — Numpy, Matplotlib, Pandas, Scikit-learn.
Learning outcomes. Having taken part in this module successfully, students can explore a dataset with Python, transform data for further processing, pose research questions about a dataset, select subsets of data for further processing, merge suitable datasets, describe and visualize findings in data using statistical terminology and the Pandas and Matplotlib packages, take data protection into account when processing data, take ethical questions into account when processing data, fit statistical models to data using Scikit-Learn, judge the quality of models, and use statistical models to classify and predict further data.
The module is worth 10 ECTS — lecture 4 SWS, exercise class 2 SWS, 300 hours of work — and is assessed by a written exam, usually 90 minutes, with admission earned through active participation in the exercises. Formally, it requires having passed the Programming module and two of the three Mathematics for Computer Science modules, or Linear Algebra I and Analysis I in their place. Unlike the other three courses here, Data Science is a compulsory module in the current bachelor programme (PO 2021); the earlier PO 2013 and PO 2016 have it as an elective.
When it runs again
The handbook schedules the module for every summer term, and it has run in each one since 2022. The last run was summer 2026 (with Michael Heck); the next one is set for summer 2027, with lectures held by Prof. Dr. Milica Gašić and the exercise class by Konrad Völkel. Earlier runs, in the university's course registry (in German): summer 2025 (with Andreas Abels, Michael Heck), summer 2024, summer 2023, summer 2022.
Data Science is one of the courses I teach at HHU Düsseldorf; the others are on the teaching page.