Written exam (in case of a small number of participants: oral tests)

This course equips students with concepts and tools that should be familiar to anyone working with (large) data. Based on practical experiments and examples, frequently encountered pitfalls and problems are discussed alongside best practices. We encounter basic statistical notions and problems of bias, testing and experimental design. Foundational methods of machine learning and statistical data analysis are employed to employ these ideas in practice. We will also discuss best practices for scientific data presentation and documentation—how to make expressive figures and tables and perform reproducible experiments—and explore ethical and technical considerations in the context of fairness and transparency.
Apart from mathmatical derivations, the exercises put a focus on practical programming. In particular, they contain implementations of some content of the lectures.

Students develop a sensitivity for common problems and misconceptions in empirical work with data. They understand the mathematical, epistemological, ethical, technical and social challenges surrounding the use of data, and know best practices to address them. They also collect a concrete box of software tools to collect, document, explore, visualize, and draw conclusions from structured, large, small, corrupted and expensive data.

Literatur / Literature: Wird zu Beginn des Semesters mitgeteilt. / Will be listed at the beginning of the semester.

Teilnahmevoraussetzungen / Course prerequisties: Grundlegende Kenntnisse in Mathematik und Programmierkenntnisse, wie bspw. durch einen B.Sc. in Informatik erworben. / Only basic math and coding skills as provided by the BSc Computer Science.