Master Data Science: Mareike van Elsacker - "Data is not non-discriminatory"

2024-01-15 The social pedagogue obtained a university certificate in "Data Analytics" at the Leuphana Professional School and is now completing a part-time Master's degree in Data Science. She wants to use the knowledge gained from her studies in social science research and critically scrutinise data and algorithms.

Mareike van Elsacker ©Hanna Lee
"Among other things, we are looking at machine learning, statistics, mathematics and the digital transformation of companies," reports the student Mareike von Elsacker.

Mareike van Elsacker has actually always worked with data. The social pedagogue conducted professional research in various projects on issues such as gender equality and violence. "At some point, I realised that something was missing from my toolbox. I asked myself how I could expand my knowledge of quantitative social research," she recalls. Mareike van Elsacker looked for extra-occupational formats to further her education. She came across the "Data Analytics" certificate programme. In three modules, students acquire basic knowledge in the fields of statistics, programming with Python and practical data analysis. "The support was excellent and studying in interdisciplinary groups was exciting. My learning curve rose steeply," reports Mareike van Elsacker. She initially had respect for the challenge: "Many of the students already work in IT. But the lecturers are always open to questions," says the political science graduate. She now holds her university certificate in her hands. Mareike van Elsacker was impressed by the content and implementation of the programme. She is now studying for a part-time Master's in Data Science at the Leuphana Professional School. The Data Analytics certificate attests to her specialist knowledge for the Master's in Data Science at the Professional School: "The certificate also gave me the opportunity to get a taste of the subject of data science," she says. Mareike van Elsacker currently works at the Schleswig-Holstein Women's Counselling Association. She has a broad professional background and also works internationally: As a Digital Ambassador at the Society for International Cooperation, she set up a database on violence and conflict research in Bogota: "There is a lot of material on the armed conflict in Colombia. In order to be able to evaluate the material in peace research, it needs to be systematised." GIZ works internationally on behalf of various ministries of the Federal Republic of Germany. In the BMBF research project "CONNECT-ED - Ways out of social isolation through encounters in the context of new media", the aim was to improve the participation of older people through digitalisation. Among other things, Mareike van Elsacker trained tutors and developed a concept for handouts. Later, in the EU research project "Baltic Gender", she investigated how female careers in marine research can be promoted: "For a long time, marine research was seen as a field dominated by men. This has changed significantly in recent years. At sea and on land, women and men from all over the world are working together on interdisciplinary projects. However, the situation is different at management level: After the doctorate, the proportion of women drops noticeably. This is particularly evident in professorships, ship management and in the management of technical departments," explains Mareike van Elsacker. The study results show: Flexibility and New Work could bring more women into management positions.

Mareike van Elsacker worked professionally with qualitative interview studies and analysed data quantitatively. She will soon be completing her first semester on the Data Science programme. Her box of methods is filling up more and more: "Among other things, we are looking at machine learning, statistics, mathematics and the digital transformation of companies," reports the student. She wants to apply the knowledge she has acquired in social science research and take a critical look at data and algorithms: "In modern data analysis, the problem is usually not the algorithms, but the data on the basis of which they learn . Data about people is often collected mainly in certain milieus. The resulting system therefore does not learn objectively about the entire breadth of society, which means that it can make incorrect decisions and, in extreme cases, discriminate strongly against certain groups. I would like to help improve this situation and make systems as error-free and non-discriminatory as possible."

With the part-time Master's degree course in Data Science, students acquire the methodological foundations and skills to exploit the potential of this data and use it profitably in organisations. In three semesters, participants deepen their knowledge in the field of data analysis and learn to process and analyse large and complex data sets using modern IT infrastructures with advanced and up-to-date methods of data analysis and machine learning - such as deep neural networks or probabilistic graphical models.