I attended the Summer School for Data and Algorithms in Leuven, Belgium. This Programme was hosted by KU Leuven, Google and other renowned research institutions. Scholars and senior industry scientists were invited to speak about their cutting-edge research at the summer school. Furthermore, the discussions included but were not limited to practical techniques in data science tools such as MySQL, NPL (natural language processing) and its applications in social science, data cleansing and database imputation, and data analysis models ranging from traditional DID to regression discontinuity design. Many papers dealt with big data in social and political science. One study, for example, used machine learning to analyse a wide range of public policy documents concerning patents.

Also, this summer school offered a combination of talking with academic researchers and industry experts. This was appealing to me as a student preparing for a quantitative thesis project and looking for career opportunities as a data analyst in NGOs or public sectors. In addition, I used the Difference-in-Difference model to complete previous research on Spanish local election turnout and the COVID-19 pandemic. So I  was able to re-access the data sample and fixed effects with other participants. In all, this intensive data summer school was an intellectually challenging but enriching experience. And I am very grateful that the Career Development Fund helped support my participation.

By Mingyue Feng