Department of Statistics
In statistics, data are essential for robust analysis and data-driven decisions. Research data encompass a variety of vast information from diverse fields such as economics, politics, society, and science, empowering researchers to identify trends, analyze patterns, and explore complex connections. The Department of Statistics at LMU Munich offers a comprehensive suite of Open Science services with a particular focus on data analysis.
The Statistical Consulting Unit (StaBLab)
StaBLab is dedicated to providing statistical consulting services to researchers and students at LMU. The team primarily focuses on assisting LMU students with their statistical needs, particularly in the context of their thesis projects, and is passionate about fostering interdisciplinary collaboration among students. Beyond that, their services are extended to researchers at LMU and other universities and various institutions.
StaBLab aims to promote the appropriate use of statistical methods in research, industry and society. In particular, the efficient transfer of new scientific findings in the field of statistics from theory into practice is one of the StaBLab’s main objectives.
Open Science Initiative in Statistics (OSIS)
The initiative is committed to supporting Open Science principles within the field of statistics. These primarily encompass the reproducibility and replicability of research results, the publication of research according the principles of Open Data and Open Access, and commitment to good empirical scientific practices.
Machine Learning Consulting Unit
R and Python Training of the Essential Data Science Training GmbH
Essential Data Science GmbH, a spin-off of Ludwig-Maximilians-Universität München (in German only), offers a comprehensive range of training courses in data science, machine learning, and statistics. The courses emphasize both the practical application of the presented methods (using programming languages such as R or Python) and a thorough understanding of the theoretical foundations of these techniques. It is aimed at users from all disciplines who want to better understand, learn and apply methods from these fields to data analysise, visualisation, modelling and prediction.
