3 Questions for

Ulf Brefeld

Professor for Machine Learning at the Leuphana University Lüneburg and member of Plattform Lernende Systeme

3 Fragen an Ulf Brefeld

Data Science: "Training exactly the specialists we need"

A look at the job exchanges shows: Data Scientists are desperately wanted. They are needed in business and science wherever large amounts of data need to be collected, processed, edited and analyzed. The progressive digitalization of all areas of life and improved methods of artificial intelligence will greatly increase the demand for data scientists in the future. The Gesellschaft für Informatik e.V. (Society for Computer Science) has defined the skills that need to be taught at colleges and universities, but also through continuing education courses. (GI) in cooperation with Plattform Lernende Systeme. Co-author Ulf Brefeld, Professor for Machine Learning at the Leuphana University of Lüneburg and member of Plattform Lernende Systeme, explains why this analysis is based on very different target groups.


Data Scientist is considered by some as the most attractive profession of the 21st century. Why?

Ulf Brefeld: We generate an unimaginably large amount of new data every day - from satellite images that are transmitted to earth to holiday photos that we upload to a social media platform. The possibility of creating added value from (heterogeneous) data is huge: road maps have evolved, for example, by aggregating additional data sources to create navigation systems and restaurant guides. To actively participate in this development and to realize your own ideas is very attractive.


What content must be taught at universities to train data scientists for industry and research?

Ulf Brefeld: The basis of a data scientist is a solid knowledge of mathematics, statistics and computer science. This is the basis for further topics such as databases, Artificial Intelligence and Machine Learning. The right mix of these components depends on the students' professional orientation. For example, students of cultural studies or engineering sciences bring different prerequisites to a data science degree course - and after their studies they will also work on very different questions and with different types of data. These differentiations should be taken into account in a data science degree program so that we can train exactly the specialists we need. In a working paper that Plattform Lernende Systeme has written together with the German Informatics Society, we define various personas that represent the different prerequisites and aspects of academic education.


How can Data Science be integrated into continuing education?

Ulf Brefeld: The current demand for data scientists cannot be met by university education alone. Dedicated continuing education programmes are particularly important so that employees in companies as well as job seekers can acquire further qualifications accordingly. They have a different profile than students: By working in practice, the theoretical foundations may have been lost somewhat and replaced, for example, by many years of expertise in practical software development. We also address this in our working paper and define suitable contents for a successful further education of employees to become data scientists.


The working paper "Data Science: Learning and Training Content" of the Gesellschaft für Informatik e.V. (GI) and Plattform Lernende Systeme is available for free download here (in German).

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