“Data science” introduces modern computational methods to manage, process, and visualize data and to communicate their results. This comprehensive program combines the latest research results with findings and challenges from practice. Participants will learn about fundamental statistical principles, broadly useful statistical methods, state-of-the-art prediction methods from machine learning, and optimization and randomization tools that help them to tackle large-scale data-analytic problems.
Our experienced lecturers demonstrate the relevance of the developed skills in selected application examples. The hybrid format combines networking opportunities on-campus with digital elements
Why this program?
In 2018, the volume of digital data generated worldwide was 33 zettabyte. This number is expected to increase to 175* by 2025. More than ever, the essential challenge for companies and organizations is to gain knowledge out of this huge amount of data and to be able to make strategic decisions on its basis. Well-trained data scientists play a central role in this process.
Our unique certificate program was developed in close cooperation with professors from TUM’s Department of Mathematics – one of Europe’s leading institutes at the interface of mathematics and its applications. Our experts support participants in building core business relevant competencies in Data Science – from developing profound methodological competencies to providing a deep understanding of statistical principles.
This unique certificate program, which was developed by the TUM Institute for LifeLong Learning in close cooperation with professors from the TUM Faculty of Mathematics / core members of the MDSI, will start in June.
*Source: Statista 2021/IDC: https://t1p.de/cun7
- Provider: TUM Institute for LifeLong Learning
- Certificate: After successful completion of the final exam, participants will receive a certificate from the Technical University of Munich.
- Duration (in weeks): 4
- Language: English
Prof. Dr. Matthias Scherer, Chair of Risk and Insurance, TUM
Prof. Dr. Mathias Drton, Chair of Mathematical Statistics, TUM
Module 1 and 2: 24th & 25th June 2022 (TUM Garching)
Module 3/ part I: 1st July 2022 (virtual)
Module 3 / part II: 2nd July 2022 (virtual)
Module 4 / part I: 8 July 2022 (virtual)
Module 4 / part II: 9th July 2022 (virtual)
Module 5 and 6: 22nd & 23rd July 2022 (TUM Garching)
- Target group: Professionals that wish to develop or deepen existing expertise in Data Science, as they e.g. currently hold or wish to gain a position as Data Scientist/Analyst in Consulting, Finance, Insurance or Tech Sectors, among others.
- Format: blended-learning
- Location: Munich/Garching near Munich/Online
Program fee and discounts:
Final application deadline: 12th June 2022
3,500 €, 10% discount for TUM alumni and employees of BMW Group in Germany.
Other conditions apply for mathematical doctoral students at TUM.
*Discounts can only be used individually
- Access requirements: Participants should hold a profound education in mathematics, computer science or a closely related field. All levels of professional experience are welcome.
*Based on our experience, the German tax benefits help many of our participants to self-finance their education as these can be worth of up to 50% of tuition fees and program related travel costs. Please, consult your personal tax advisor for more details. For participants of our programs residing outside Germany this might be applicable, please check the situation with the local tax authorities in your country of residence.
- An introduction to R, R Studio, and tidyverse
- Data management
- Data visualization
- Creating reports with markdown tools
- R interfaces with other languages (julia, python)
- Designing experiments and modeling data
- Linear regression
- Likelihood and Bayesian inference
- High-dimensional regression
- Generative and discriminative approaches to classification and regression
- Logistic regression
- Generalized linear models
- Classification with logistic regression and discriminant analysis
- Unsupervised Learning
- Clustering with k-means/k-medians, mixture models, stochastic block/ball models
- Dimension reduction with PCA/SVD
- Manifold Learning
- Kernel methods:
– support vector machines,
– Gaussian processes
- Decision trees
- Ensemble methods:
– boosting and random forests
- Neural networks and deep learning:
– Training neural nets
– Approximation theory
– Network architectures
- Reinforcement learning:
– Markov decision processes
– deep RL
- Non-linear optimization
- Convex optimization
- Stochastic gradient methods
- Randomization and sketching
- Presentation of case studies that exemplify applications in selected areas:
– Financial and Actuarial Math
– Examples from TUM Data Innovation Lab
- Assessment of your participation in the program in a pass/fail exam
- Prof. Ph.D. Claudia Czado, Chair of Applied Mathematical Statistics/ MDSI, TUM
- Prof. Dr. Mathias Drton, Chair of Mathematical Statistics/ MDSI, TUM
- Dr. Stephan Haug, Chair of Mathematical Statistics, TUM
- Prof. Dr. Blanka Horvath, Chair of Mathematical Finance/ MDSI, TUM
- Prof. Dr. Oliver Junge, Chair of Numerics of Complex Systems, TUM
- Prof. Dr. Christian Karpfinger, Chair of Algorithmic Algebra, TUM
- Prof. Dr. Felix Krahmer, Chair of Applied Numerical Analysis/ MDSI, TUM
- Prof. Dr. Christina Kuttler, Chair of Mathematics in Life Sciences, TUM
- Cláudio Mayrink Verdun, Chair of Applied Numerical Analysis, TUM
- Prof. Dr. Matthias Scherer, Chair of Risk and Insurance, TUM
- Prof. Dr. Elisabeth Ullmann, Chair of Scientific Computing and Uncertainty Quantification, TUM
- Prof. Dr. Michael M. Wolf, Chair of Mathematical Physics, TUM
Are you interested in this certificate or similar programs? Stay up to date!
On-campus courses and Covid-19
We look forward to resuming our on-campus Certificate Programs within the first months of 2022 and to welcoming many of our participants in person. However, we will continue to follow safety-measures and restrictions introduced to help combat the Covid-19 pandemic. This may require that we deliver some modules in a virtual or blended format. We will ensure registered participants are kept informed about any changes required and will strive to provide a safe environment and a seamless learning experience. If you have any questions about the current situation, please do not hesitate to contact one of our program managers.