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Data Science

Language: Englisch
Location: Garching bei München, Remote
Duration: 6 days in 4 weeks
Start: 21.06.2024
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

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Quick Info

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

Benefits

  • 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.

  • 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.

  • 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 audience: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. Different conditions apply for mathematics doctoral students at TUM.
Dates:21.-22.06.2024 + 05.-06.07.2024 + 19.-20.07.2024
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.

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.

Module 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)

Module 2: Statistical Foundations of Data Science

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

Module 3: Part 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

Module 3: Part 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

Module 4, Part I: Predictive Approaches in ML

Kernel methods:

  • support vector machines,
  • Gaussian processes
  • Decision trees

Ensemble methods:

  • boosting and random forests

Module 4, Part II: Predictive Approaches in ML

Neural networks and deep learning:

  • Training neural nets
  • Approximation theory
  • Network architectures

Reinforcement learning:

  • Markov decision processes
  • deep RL

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

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

Module 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 developed in collaboration with the Department of Mathematics of the Technical University of Munich.

Programm Managerin Anja Brankovic

Your contact

Anja Branković
Program Manager

Phone: +49 (89) 289 – 28479
E-Mail: datascience@lll.tum.de

Get in touch

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