Data science

Data science, also known as knowledge discovery in databases (KDD), is an automated process to discover new and interesting information in large quantities of data. Data science imparts the principles of data management and analysis and advanced methods of information preparation and visualisation as well as the required basic principles of core computer science. As the quantity and complexity of stored data from science and industry continues to increase, the need for intelligent machine and expert-supported analysis methods of this data also increases. Due to the high demand for data mining, it has become an interface for a variety of areas of research, such as machine learning and information visualisation, artificial intelligence and human computer interaction. Naturally, the basic principles from the standard areas of computer science still apply, for instance in regard to databases, algorithms and software engineering.

Module overview

We provide a list of selected courses which fit to the specialization "Data Science" here. Please check ZEuS for the offers of the current and upcoming semester.

Basic modules

The following modules should be completed as a basis for advanced modules, if they (or equivalent modules) have not been completed in a previous bachelor’s programme:

  • Data visualization: Basic concepts
  • Data mining: Basic concepts

Additional basic modules

Additionally, other basic modules fit to this specialization and we recommended to complete some of them, if they (or equivalent modules) have not been completed in a previous bachelor’s programme. The recommended basic modules include:

  • Introduction to machine learning
  • Document analysis: Computational methods
  • Algorithm engineering
  • Big data management and analysis
  • Deep learning in computer vision
  • Multimedia retrieval: Basic concepts
  • Applied visual analytics
  • Geografic information systems

Please see ZEuS for more details and the courses that are offered in the current or upcoming semester.

Advanced modules (purely master's level)

As the exam regulations specify, you need to complete at least three advanced modules in one area to be able to have a specification stated on your examination certificate. For the specialization in “Data science”, a range of advanced modules are offered. These include:

  • Data mining: Advanced topics
  • Data visualization: Advanced topics
  • Multimedia retrieval: Advanced topics
  • Graph data management and analysis

Please see ZEuS for more details and the offers of the current or upcoming semester.

Courses from other departments and key qualifications

Data science has a great variety of application areas, as can be seen by our Excellence Cluster “Collective Behaviour”, for example.

The following courses from other departments  provide you with an insight into these application areas:

  • from the Department of Biologie, e.g.: Evolution, behaviour (Evolution, Verhalten, taught in German)
  • from the Department of Linguistics, e.g., Structure and history of English, Finite state morphology, Grammar development
  • from the Department of Psychology: Social psychology (Sozialpsychologie, taught in German)

For further suitable courses from other departments and key qualifications, see the general list provided by the department or contact your mentor.

Career prospects

You will acquire the following skills…

will follow soon

We have contacts to the following companies...

For contacts to companies with which you could possibly do an internship with, please contact the research groups below.

You could work as…

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Mentor recommendations

Additional Information

The Powerwall

Daniel Keim’s working group uses a 5.20 m x 2.15 m Powerwall, which is unique in Germany and provides completely new perspectives for the area of visual data exploration.

KNIME

KNIME (http://www.knime.org/), pronounced [naim], is a modular data exploration platform that enables data flows, so-called "pipelines", to be visually combined. These are then executed, allowing the data to be "pumped through", which in turn allows for the inspection of the results in interactive views of data and models.

KNIME was (and continues to be) developed at the Chair for Bioinformatics and Information Mining. Michael Berthold's working group utilises this platform for teaching and research purposes. Almost all the data mining methods developed by the working group have been integrated into KNIME.