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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, 4 April 2022, 1215-1600 with a break: online lecture room.

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


Technical setup test: tba.

Pre-exam meeting: Wednesday, 10 August 2022, 1000-1200, online lecture room.

Exam1: Friday, 12 August 2022, 1000-1300, tba.

Post-exam1: individual exam reviews.

Exam2: Friday, 30 September 2022, 1300-1600, tba.

Post-exam2: individual exam reviews.



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