Data Science is everywhere at the ZHAW. The ZHAW Datalab is a group of experts from all departments, solving practical problems with data. And the new Bachelor’s program “Data Science” at the ZHAW School of Engineering has started well in the last semester. But what does the career of a data scientist look like? Catherine Kosten gives some insights.
Catherine Kosten works as a research assistant in the ZHAW School of Engineering and pursues a PhD in Computer Science at the University of Fribourg. At the ZHAW she is involved in the EU Inode project where she works on data democratisation via natural language interfaces for databases. In the interview, she shares her path into Data Science and gives advice to students interested in the field.
How did you become a data scientist?
I had an unconventional path into Data Science. I have bachelor’s degrees in both Fine Arts and Linguistics. I was always interested in computers, but never considered tech as a career path. When I finished my bachelor’s degrees, I moved to France and after a bit of research on what kind of further education I could pursue with a degree in Linguistics, I found Natural Language Processing (NLP). I did my master’s in NLP in France and then I worked as a Data Scientist for a couple of years in industry before deciding to pursue a PhD in the field and joining the ZHAW.
The ZHAW School of Engineering now has a new Bachelor’s degree programme on Data Science. What skills do the students need to succeed in this field?
I think potential students might be intimidated by the technical or mathematical side of data science. Technical skills and math are indeed required in the field, but that’s only one aspect in the larger scope of Data Science. In addition, it makes a world of difference when you are learning these concepts and applying them directly versus learning them in a purely theoretical environment as is often the case in high school math classes.
What is a benefit of hiring a diverse team of data scientists?
The benefits of hiring a diverse team are extremely well established across industries. Diverse teams have been shown not only to improve financial outcomes for companies, but also to have higher rates of innovation and higher individual performance as well.
In data science specifically, to design products that meet the needs of the whole population, you need the perspectives of a diverse group of people from different walks of life working to develop these products. As a Data Scientist who also happens to be a woman, the type of diversity that I am focused on is promoting and supporting other women and gender minorities who are interested in or already working in Data Science. This is the reason why I joined the Zurich chapter of Women in Machine Learning and Data Science.
Which other networks are you involved in?
These networks do a great job of promoting interdisciplinary collaboration. Academia and industry can be quite isolated from one another, and sharing information is key to moving forward in the field. Participating in these networks keeps me connected to and aware of the real-world issues that industry is facing as well as the work that other research groups at the ZHAW are doing in Data Science.
Event on 30 March: “A career in data science – myths and obstacles in a rapidly growing field”
In an online event on 30 March, Zurich Women in Machine Learning and Data Science partner with CLAIRE to talk about what it means to be a Data Scientist in a world full of possibilities. The panellists will be answering questions about hiring diverse data science teams, working in interdisciplinary data science and the experience of working in a typically male-dominated field. Catherine Kosten is one of the co-organisers and will be moderating the panel. Bring your own questions to the live Q&A with our guests! Register here.