Data Science

Language:	English
Location:	garching_bei_munich, remote
Duration:	6 days in 4 weeks
Start:	Summer 2025
Cost:	3.500 €

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Given the explosive growth of digital data worldwide, gaining insights from this data and making strategic decisions is crucial for companies and organizations. While the volume of digital data generated worldwide amounted to 33 zettabytes in 2018, the volume was already 126* in 2023 and is expected to rise to 284* zettabytes by 2027. Well-trained data scientists play a central role in this.

Our certificate program offers a unique opportunity to learn modern computational methods for data analysis, prediction, and visualization. Developed in close collaboration with renowned professors from TUM’s Department of Mathematics and experts from the field, our program offers a comprehensive curriculum that combines theoretical knowledge with practical application. With a strong focus on current research findings and real-world challenges, our learning objectives are designed to equip you with the necessary skills to succeed in the fast-paced world of data analytics.

*Source: Statista 2024/IDC: https://t1p.de/cun7

Quick Info

Here you will find all the information you need for this course.

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Benefits

01

1

Building core business relevant competencies

Our program equips you with competitive core competencies for data-driven industries – from developing sound methodological skills to a comprehensive understanding of statistical principles.

02

2

Cooperation with experts

The program was developed in close cooperation with professors from the Department of Mathematics at TUM, one of Europe’s leading institutes at the interface of mathematics and its applications. You will learn from first-class experts.

03

3

Blended learning format

In a hybrid learning setup, connect with peers and cross-industry professionals for
collaborative growth while also having flexibility due to online sessions.

Program Overview

You can find all the important information about the certificate program here. Below you can find out the objectives of the program, the exact details of the process, what you will learn and which lecturers will teach you the content.

Aims of the certificate course
Here you will find information on the aims of this course and its content.

The certificate program is designed to provide you with a solid understanding of modern computational methods for analyzing, predicting, and visualizing data. You will learn fundamental statistical principles, broadly applicable statistical methods, modern prediction methods from machine learning, and optimization and randomization tools through a combination of theoretical concepts and practical application. The program aims to equip you with the skills to solve big data analytical problems and make informed decisions based on data.

Details
Here you will find information on dates, requirements and other general conditions for the certificate program.

Program:

Data Science

Graduation:

Participants receive a certificate from the Technical University of Munich after successfully passing the final exam.

Academic Responsibility:

Prof. Dr. Matthias Scherer, Chair of Risk and Insurance, TUM
Prof. Dr. Mathias Drton, Chair of Mathematical Statistics, TUM

Target group:

Experts who want to expand or deepen their expertise in data science, e.g., because they currently hold or aspire to a position as a data scientist/analyst in consulting, finance, insurance, or technology.

Program fee:

3,500 Euro*

Language:

English

Discounts:

10% discount for TUM Alumni and members or employees of our strategic cooperation partners (see below).

Dates:

Summer 2025

Study location:

Munich near Garching & Online

Format & Timing:

Part-time, presence & digital, 6 days in 4 weeks

Admission requirements:

Participants should have a sound mathematics and computer science education or a closely related field.

* In our experience, tax benefits in Germany help many of our program participants to finance their education, as they can declare up to 50% of tuition fees and program-related travel expenses in their tax return. Please speak to your tax advisor for an assessment of your situation. This may also apply to participants of our programs who reside outside of Germany; please clarify the situation with the local tax authorities.es in your country of residence.

Lecturers
Get to know our experienced lecturers who will teach you the certificate content and with whom you will work.

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. 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

Structure
Find out here how the course is structured and what the individual modules of the certificate program contain.

Modul 1: Computing with Data

An introduction to R, R Studio, and tidyverse
Data management
Data visualization
Creating reports with markdown toolsR interfaces with other languages (julia, python)

Modul 2: Statistical Foundations of Data Science

Designing experiments and modeling data
Linear regression
Likelihood and Bayesian inference
High-dimensional regression

Modul 3: Teil I: Basics of Supervised Learning

Generative and discriminative approaches to classification and regression
Logistic regression
Generalized linear models
Classification with logistic regression and discriminant analysis

Modul 3: Teil II: Basics of Unsupervised Learning

Unsupervised Learning
Clustering with k-means/k-medians, mixture models, stochastic block/ball models
Dimension reduction with PCA/SVD
Manifold Learning
Autoencoders

Modul 4, Teil I: Predictive Approaches in ML

Kernel methods:

support vector machines,
Gaussian processes
Decision trees

Ensemble methods:

boosting and random forests

Modul 4, Teil II: Predictive Approaches in ML

Neural networks and deep learning:

Training neural nets
Approximation theory
Network architectures

Reinforcement learning:

Markov decision processes
deep RL

Modul 4, Teil II: Predictive Approaches in ML

Neural networks and deep learning:

Training neural nets
Approximation theory
Network architectures

Reinforcement learning:

Markov decision processes
deep RL

Modul 5: Optimization and Randomization for Large-Scale Data Analysis

Non-linear optimization
Convex optimization
Stochastic gradient methods
Randomization and sketching

Modul 6: Case Studies & Final Exam

Presentation of case studies that exemplify applications in selected areas:

Financial and Actuarial Math
Examples from TUM Data Innovation Lab
BioTech.

Assessment of your participation in the program in a pass/fail exam

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Partners

The program was created in collaboration with the Faculty of Mathematics at the Technical University of Munich.n digital.ING.

Your contact

Anja Branković

Program Manager

Phone: +49 (89) 289 – 28479

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Data Science

Quick Info

Benefits

01

1

Building core business relevant competencies

02

2

Cooperation with experts

03

3

Blended learning format

Program Overview

Partners

Application for Data Science

Your contact

Other programs that might also interest you

Learn object-oriented Programming

Data Science

Introduction to MATLAB