Career of the futureA position as a data scientist is viewed as the best future career. In several areas decision-makers use data in order to achieve b
alanced policies. Through the application of various data science methods, our research group conducts applied research aiming provide valid and practically-feasible solutions.
How data science can help tackle modern challenges
The challenges faced in applied data science are very broad. For example:
How can we support policies with the transition towards more sustainable energy resources? How can we use data to make ‘smart mobility’ even smarter? How can we increase participation of civilians and improve the relevant policies? How can we support decision-making - for example, in the case of suddenly occurring pandemics? In the healthcare domain, how can we improve awareness and engagement of patients through the use of data?
Support through data scienceHow can the applied research of the Data Science research group support society in answering the aforementioned questions? We do this, for example, through the development of new methods for faster and better evaluation of an important vaccine; through prediction of the effects of measures aimed at limiting the spread of contagious diseases; through the prediction of the social readiness of residents to participate in energy transitions; orr through the development of new dynamic planning systems for autonomous vehicles.
Ester de Jonge
Dr. Mathis Mourey holds a Bachelor's in Economics from the UGA (France) and the NTNU (Norway), as well as a Master's in Finance from Grenoble Graduate School of Management (France). Additionally, he holds a Ph.D. in Finance from the UGA (France). His thesis work focused on the measurement of Systemic Risk in financial systems. Currently, he teaches at the Haagse Hogeschool a variety of classes on Finance, Accounting and Statistics. He also participates in the development of modules on Data Analytics and Machine Learning methods. His research in the Data Science research group focuses on the application of machine learning methods on educational datasets. The research aims to uncover the determinants of student dropout in the early stage of their academic career. Moreover, he develops web-scraping algorithms aiming at facilitating the use of Open Data. One of his current projects consists in the automatic analyzing of consumer satisfaction in email@example.com