Data Science (B.Sc.)

In the computer science modules you will learn the basics of programming and data management - to a depth that is needed in typical data science projects.

After completing the compulsory program of your advanced studies, you will know the most important basic principles of machine-learning systems and will be able to carry out data analytics projects independently as well as to work with common Big Data architectures.

The electives and domain projects in the 6th and 7th semesters allow you to specialize and thus, for example, to deepen computer science basics in order to be able to follow a computer science master's degree, or to gain more experience in machine learning.

After the introductory module in the 1st semester, you will carry out a Data Science project in each of the 2nd to 4th semesters, during which the basic economic knowledge required for this is taught. Subjects from the areas of computer science, statistics and mathematics also pick up on this project and illustrate the respective course content using this application example.

You will spend the 5th semester as internship semester in a company, where you will usually independently carry out a Data Science project on a problem from the field of economics.

In the domain projects and electives of the 6th and 7th semesters,  you will deepen your basic knowledge in the field of economics and further expand knowledge in application domains of your choice.

In the statistics modules you will learn the theoretical foundations of data analysis and develop an understanding of how reliable your conclusions drawn from data are.

You will then use application examples to develop systematic approaches to typical data analytics problems. Machine learning also uses many of the concepts covered in statistics.

In the mathematics modules, you will learn the fundamentals of calculus, linear algebra, and logic, which are used in the economics, statistics, and machine learning and data analytics modules.

In addition, there is an emphasis on mathematical modeling, which is the ability to put real-world issues, problems, and data into a form to which the theoretical methods you learn can be applied.