Alumni

Parallel to her studies, Jasmin Classen already had a job as working student at Flixbus in the field of Network Analysis. After graduating she started there as Junior Data Scientist and now she holds the position as Data Engineer.

Why did you opt for the SEDS study program?

After studying Sociology I was interested in improving my technical and methodological expertise. I opted for the SEDS study program because its broad curriculum in Social Science, Statistics, and Computer Science was a great opportunity to combine my academic background with exactly that goal.

What did you like most of the program?

I liked the flexibility and diversity of the curriculum offering courses from different faculties. This way I was able to learn about many different subjects related to Data Science that interested me. Also I truly enjoyed our always supportive fellow SEDS student community full of smart and amazing people from many different disciplines.

How did your studies serve your professional career?

My studies in Data Science and especially also the additional knowledge in Computer Science helped me to start a career in the mobility industry as a Data Scientist and to transfer into Data Engineering later.

Timo Spinde is currently a Ph.D. student at the National Institute of Informatics in Tokyo. He specifically focuses on media bias analysis, and founded his own research network: www.media-bias-research.org.

Why did you opt for the SEDS study program?

The SEDS program allowed me to strengthen my data analysis skills. Before going to Konstanz, I studied Computer Science and Journalism, and wanted to combine both into working as data journlist.

How did your studies serve your professional career?

Mostly, the studies gave me the freedom to follow my projects. I already founded a small company during the program, and then followed up on that afterwards. Even more, the program made me even more interested in data science.

What did you like the most about the program?

The interdiscplinarity of the program was a huge advantage, and I was able to select courses based on my personal preference.

After being a software developer at CorrelAid, Jan Dix co-founded &effect in Berlin. &effect develops software solutions for effective decision-making in the public and social sector under the slogan “Data at the service of the public good”.

Why did you opt for the SEDS study program?

The SEDS study program allowed me to combine my interests for political science, data science and computer science. Additionally, due to the flexibility of the curriculum, I had the opportunity to study one year abroad.

How did your studies serve your professional career?

The foundations learned in the SEDS program helped me to start a career in software development. However, given the interdisciplinary nature of the program, fellow students started their careers in the private, public, academic, or non-profit sector as data scientists, consultants, or PhD students.

What did you like most about the program?

The SEDS program provides in-depth social science empirical research mixed with a solid introduction to programming concepts. I really appreciate how the program includes application examples from various fields in the course work and brings students from different disciplines together.

Qixuan Yang is currently a Ph.D. student in the Department of Political Science at Yale University. Previously, he worked as a senior consultant/senior data scientist, focusing on natural language processing (NLP) and machine learning operations (MLOps).

Why did you opt for the SEDS study program?

The interdisciplinary approach and a wide range of course selections in maths, statistics, computer sciences, and social sciences were very attractive. I liked how the curriculum was crafted, such that the students can grow a deep understanding of the theoretical and practical aspects of data science.

What did you like the most about the program?

I liked the flexibility of the program. It left a lot of room for the students to put together the portfolio they desire. I was able to learn about very basic math and statistics foundations for data science, as well as conduct empirical (applied) research on many occasions. In addition, there is positive peer pressure: I was very happy to study with other brilliant colleagues from whom I could learn a lot.