Today’s society operates on large amounts of data. Industry, governments and academia are asked to provide insight into these data.
•But how do we deal with such large amounts of data?
•What techniques do we use to mine the data?
•What are the legal and ethical aspects regarding these data sets?
•And what economic value can be found in big data?
The MSc specialization Data Science: Business and Governance trains students to become Data Scientists that can address these questions. The Harvard Business Review calls the job of Data Scientist "the sexiest job of the 21st century"!
Why Data Science: Business and Governance in Tilburg?
•Tilburg University offers a wide range of complementary expertise, including techniques for data mining, pattern recognition, business analytics, visualization and process analytics; as well as knowledge on law, regulation, ethics and entrepreneurship.
•The MSc specialization consists of courses in methods of analysis, together with economic and management as well as legal, ethical and methodological perspectives on data, all of them taught by experts in these fields.
•The Master’s specialization Data Science: Business and Governance offers (constitutes/ consists of) a well-balanced mixture of theoretical and practical (elective) courses.
These elements combine to make this specialization unique in Europe and possibly even in the world: Four schools (Tilburg School of Economics and Management, Tilburg School of Law, Tilburg School of Social and Behavioral Sciences, and the Tilburg School of Humanities) work together in offering the best possible training for the job of the future, that of Data Scientist.
Data Science: Business and Governance graduates will not only have knowledge and expertise in the area of data analysis and data mining, but also in economic, management and legal perspectives on big data.
Growing need for Data Scientists
There is a growing need in government organizations, in companies and in academia for employees with the analytical skills needed to analyze large datasets, recognize patterns, and visualize data, and combining these skills with interdisciplinary knowledge of perspectives on Data Science.