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Foundations of data science

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Data science aims at making sense of big data. To that end various tools have to be understood for helping in analyzing the arising structures.

Often data comes as a collection of vectors with a large number of components. To understand their common structure is the first main objective of understanding the data. The geometry and the linear algebra behind them become relevant and enlightening. Yet, the intuition from low-dimensional space turns out to be often misleading. We need to be aware of the particular properties of high-dimensional spaces when working with such data. Fruitful methods for the analysis include singular vector decomposition from linear algebra and supervised and unsupervised machine learning. If time permits we also consider random graphs which are the second most used model for real world phenomena.


Michael Nüsken

Time & Place

First meeting: Monday, 3 April 2023, 1215-1600 with a break: b-it 0.107 and online lecture room.

To ease your communication you can at any time appoint with each other in this free room.

Exam tools and materials

You must be part of the group '| 2023 SoSe | MA-INF 4228 - Foundations of Data Science - Technical test' within the eCampus course to get access. This will be the basis for the exam access list, so: If you are registered for the exam but you do not have access, contact me now!

Needed tools for the exam:

Allowed material:

Detailed information is collected here.


Technical setup test and Pre-exam meeting: Wednesday, 12 July 2023, 1200-1400, online room.

Exam1: Friday, 11 August 2023, 1000-1300, online (eCampus, BBB).

Post-exam1: individual exam reviews.

Exam2: Friday, 29 September 2023, 1000-1300, online (eCampus, BBB).

Post-exam2: individual exam reviews.



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