The ABP mastertrack Smart Mobility Data Science and Analytics (SMDA) , starting in september 2021, is part of the EIT Urban Mobility Master School,, which trains students to extract meaningful insight from ever-increasing urban mobility data.
Study objectives
Smart Mobility Data Science and Analytics (SMDA) is a master track where students learn how to manage large mobility datasets, critically understand their quality, and utilize them to address our cities most pressing challenges. You will gain analytical skills to extract hidden patterns of big mobility data and learn advanced statistical methods to build forecasting models which will be used as planning tools. You will apply the knowledge you gain in projects during which you will work on real mobility challenges.
Study structure
The track is a double degree program. You will spend one year each at two different universities--the “entry” university and the “exit” university. All choices are located at a leading university in Europe in the field of urban mobility: TU/e, UPC Barcelona and University of Tartu Estonia. Graduating students will be awarded two master’s degrees, one from each university where they carried out their studies.
The track has integrated technical content and business content (Innovation & Entrepreneurship – shared between all programs). An integral part of the second year is a Master’s thesis (30 ECTS).
The Smart Mobility Data Science and Analytics programme is designed in an interdisciplinary fashion and emphasizes new and emerging transportation technologies and services for citizens, goods, and logistics. Furthermore, as an EIT Urban Mobility Master School program, it integrates entrepreneurship & innovation throughout the coursework and was launched to train future elite data scientists in urban mobility and innovation
Overview of the track SMDSA
More information about the curriculum of the track SMDSA can be found here. On this page you can also download the ABP-CME course overview (including electives and detailed information like timeslots).