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Curriculum: Starter Track

Statistical thinking


Data science approaches are based on statistical/mathematical methods as well as computer science competences. In this context, it is crucial to understand the basic principles of statistical methods. This will help to adequately apply statistical methods and to produce reliable statistical results.

Learning contents

This course provides an introduction into statistical basics and concepts relevant for data science applications. After a brief presentation of the categories of statistics (descriptive, predictive, confirmatory) and their general ideas, selected basic methods will be explained and illustrated by practical examples: concept of probability, parameter estimation, confidence intervals and testing of hypotheses.

Learning outcomes

A basic understanding of the major statistical principles.

Prior knowledge


Further reading

- Fahrmeir, Heumann, Künstler, Pigeot, Tutz (2016). Statistik – Der Weg zur Datenanalyse, 8. Auflage, Springer-Verlag, Berlin, Heidelberg.

- Fahrmeir, Künstler, Pigeot, Tutz, Caputo, Lang (2009). Arbeitsbuch Statistik, 5. Auflage, Springer-Verlag, Berlin, Heidelberg.

- Freedman, Pisani, Purves (1998). Statistics, 3rd edition, W.W. Norton and Company, New York.

- Spiegelhalter (2019). The Art of Statistics: Learning from Data, Pelican, London.


February 8, 2024, 9:00 AM - 12:00 PM


Haus 2 (Oxford)
Small seminar room, 4.3250 (3rd floor)

Mary-Somerville-Str. 2
28359 Bremen

Online via Zoom

Pigeot, Prof. Dr. Iris

Scientific director of theLeibniz Institute for Prevention Research and Epidemiology – BIPS and head of the Department of Biometry and Data Management

Professor for Statistics with a focus on Biometry and Methods in Epidemiology at the University of Bremen


Lecturers information