Professional Training
«The program offers a comprehensive insight into a variety of data science topics. Beyound teaching important theoretical foundations, the program also includes some exercises and use cases to illustrate the usefulness of the presented methods.»
Dr. Giuseppe Casalicchio
PostDoc at MCML, Lecturer
Data Science Certificate Program at LMU Munich
The Data Science Certificate Program is a graded extra-occupational training program at LMU Munich, which is organized twice a year (summer term and winter term), once as an in-person course (on Fridays) and once as an online course.
The Data Science Certificate Program provides practical and scientifically grounded knowledge of the theory and methods of Data Science and addresses the needs of professionals in their daily work. This program is designed to benefit individuals working in any area of business, research, or education. It is offered twice a year as a one-semester training with an estimated workload of 200 hours - including time for preparation, follow-up work, and preparation for the exam.
Target Group
Professionals and executives who want to continue their education in the field of data science, want to learn how to extract information from data, rethink and enhance data analysis and machine learning procedures to prevent wrong decisions.
Requirements
- University degree and work experience:
- Either a university degree with focus in mathematics, statistics, computer science or related disciplines and at least one year of work experience, or
- Any kind of university degree qualifying and at least three years of work experience in an area relevant to the certified data science training program.
- For many practical sessions, basic knowledge of the statistical programming language R is required (in some practical sessions, we may additionally provide equivalent Python code; however, our primary emphasis remains on utilizing R in all practical code examples).
Lectures and Topics
- (1) First Steps in Data Analysis
- (2) Statistical Foundations
- (3) Statistical Modeling
- (4) Large-Scale Data Science and AI
- (5) Unsupervised Methods
- (6) Supervised Machine Learning 1
- (7) Supervised Machine Learning 2
- (8) Supervised Machine Learning 3
- (9) Introduction to Deep Learning
- (10) Written and Oral Exam
Lecturers
- Prof. Bernd Bischl (Director of MCML, Department of Statistics, LMU)
- Prof. Göran Kauermann (PI at MCML, Department of Statistics, LMU)
- Prof. Dieter Kranzlmüller (Director of LRZ, Institute of Informatics, LMU)
- Prof. David Rügamer (PI at MCML, Department of Statistics, LMU)
- Dr. Giuseppe Casalicchio (PostDoc at MCML, Department of Statistics, LMU)
Practical Information
Application Deadline
Twice a year, see official website of the Data Science Certificate Program for registration and more details.
Regular Price
6.500 € (in-person course) / 5.000 € (online course)
Expected workload
About 200 hours (incl. preparation for the lectures, follow-up work, and preparation for the exam).
Language
English (unless all participants understand German well enough; course material itself will always be in English).
Outcome
Receive a graded data science certificate from the LMU, one of the best universities in Germany.
References
«A very dense course covering all areas of interest for Data Scientists with outstanding lecturers that not only give a broad overview of their topic but also allow discussions of any level of detail and in-depth. I definitely recommend the Data Science Certificate Program for anyone dealing with modeling and/or analyzing data, especially in natural science.»
Dr. Kristina Wolf, BioNTech Small Molecules GmbH
«I am not working with data on a regular basis. Data Science and Machine Learning were just buzzwords for me. However, as a mathetician I am very interested in the ideas and theories behind those buzzwords. The Data Science Certificate Program provided exactly the information I needed. Thanks to the very competent and friendly staff.»
Julian Mühlbauer, KfW
«The Data Science Certificate Program includes all essential fields a professional data scientist should cover. It emphasizes the proper understanding of the most important mehods and enables the participants to choose the appropriate method from a rich toolbox. The lecturers strike a good balance between background theory and real-life applicability.»
Yanko Punchev, Tecta Invest