Master in Applied Data Science

Students will acquire a rudimentary understanding of linear algebra, probability theory, information theory and their use in machine learning and data science. Paying particular attention to mathematics for information systems, this module serves as a foundation module for Machine Learning 1 & 2.

Lecturer

Prof. Dr. Jan Nagler

Using Python, this module provides you with an introduction to basic algorithms, as well as the design analysis of algorithms and data matching structures. This allows you to implement taught algorithms and learn the basics of Python.

Lecturer

Vahe Andonians

Data Analytics (or Data Science) is an emerging field in industry and academics. It covers methodologies, algorithms and processes to tackle the challenges in times of big data, where we are confronted with large amounts of high-dimensional data of different types. While the classical statistical approach has some weaknesses in this context, new ways and methods of data analysis have been established under the term machine learning. Today, they are widely used in science and practice benefitting from the calculation power of modern computer technologies.

This module provides an introduction to Data Analytics, covering computational techniques and algorithms for finding and analysing patterns, even in large-scale datasets. Topics to be covered include data preparation, integration, analysis, visualisation, segmentation, classification, prediction and decision making. You will implement and apply the methods using the programming language, Python and the related libraries.

Lecturer

Prof. Dr. Lucas Böttcher

This course introduces causal inference and generalised linear multilevel models from a Bayesian perspective. The aim of the course is to give you a hands-on introduction to the fundamentals of statistical modeling.  We will cover the basics of regression up to advanced multilevel models, focusing on the algorithmic details throughout the course to build your understanding of and confidence with model-based computational statistics.

Lecturer

Prof. Dr. Gregory Wheeler

This module serves as introduction to accounting as the language of business and its various purposes and applications. On a very fundamental level, accounting statements are a primary source of systematic public information about businesses, providing the basis for answering many relevant questions. As such, it is important for those interested in business data analytics.

External Lecturer

Dr. Heike Dengler

Nowadays everyone is aware of the ever-growing importance of the data streams fueling the economy and becoming the future catalyst for our society. Learn how to master these streams by understanding the key concepts of the most important frameworks and technologies for data storage and management.

External Lecturers

Prof. Dr. Peter Roßbach

This module is a hands-on, case-study based introduction to contemporary regression-based techniques in machine learning. Machine Learning 1 has a focus on supervised learning algorithms (used to make accurate predictions about the future from current data) and unsupervised learning (used to discover unknown structure in your current data).

Lecturer

Prof. Dr. Gregory Wheeler

The course provides an introduction to financial management, including capital budgeting and capital markets. The main focus is on designing and conducting empirical analyses in small teams.

Lecturer

Prof. Dr. Frank Ecker

This hands-on module focuses on statistical machine learning and probabilistic data analysis involving highly parameterised models. Topics include time series analysis, variational inference, graphical models and unsupervised learning. You will learn how to implement supervised and unsupervised machine learning models and gain an understanding of the computational challenges faced when performing statistical inference on high-dimensional data.

Lecturer

Prof. Dr. Jan Nagler

This module explores the ethical and legal questions that information technologies raise for issues such as privacy, responsibility or fairness. Participants will gain an in-depth comprehension of legal and ethical issues surrounding information technologies, as well as the crucial legal and ethical questions that we should ask about such technologies. On successful completion of this module, students will have developed and strengthened their analytic and critical skills, as well as their ability to apply those skills to ethical and legal problems and develop solutions to those problems.

Lecturer

Prof. Dr. Sebastian Köhler

This module gives you the latest insights into strategy development and execution with a strong emphasis on organisational and machine learning on data analytics. Students become acquainted with models, tools and techniques to develop, analyse and execute organisational strategy and its success.

Lecturer

Prof. Dr. Matthias Mahlendorf

This module covers deep neural networks, which are currently the “workhorse” of machine learning and most commonly used methods. Our main purpose will be to understand the theoretical background necessary to employ deep neural networks to solve problems of image recognition and language processing. Particularly, we focus on different theoretical concepts to make deep neural networks which are thus essential for building successful applications. The module has a practical focus, taking theory and then applying it immediately in each class.

Lecturer

Prof. Dr. Florian Ellsäßer or Vahe Andonians

This module is focused on applying machine learning techniques to gain language understanding. Natural language processing is one of the main sub-fields of machine learning and has driven major algorithmic break-throughs in recent years. Language is a form of time series so break- throughs in natural language processing such as LSTM networks have been closely connected to advances in machine learning in general.

Lecturer

Prof. Dr. Florian Ellsäßer