naa a22 4500 528782223 20180924065513.0 cr unu---uuuuu 180924e201712 xx s 000 0 eng 10.3929/ethz-b-000172373 doi 10.3233/DS-170005 doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/172373 Pournaras Evangelos Cross-disciplinary Higher Education of Data Science-Beyond the Computer Science Student [Elektronische Daten] [Evangelos Pournaras] Open access ethresearch The majority of economic sectors are transformed by the abundance of data. Smart grids, smart cities, smart health, Industry 4.0 impose to domain experts requirements for data science skills in order to respond to their duties and the challenges of the digital society. Business training or replacing domain experts with computer scientists can be costly, limiting for the diversity in business sectors and can lead to sacrifice of invaluable domain knowledge. This paper illustrates experience and lessons learnt from the design and teaching of a novel cross-disciplinary data science course at a postgraduate level in a top-class university. The course design is approached from the perspectives of the constructivism and transformative learning theory. Students are introduced to a guideline for a group research project they need to deliver, which is used as a pedagogical artifact for students to unfold their data science skills as well as reflect within their team their domain and prior knowledge. In contrast to other related courses, the course content illustrated is designed to be self-contained for students of different discipline. Without assuming certain prior programming skills, students from different discipline are qualified to practice data science with open-source tools at all stages: data manipulation, interactive graphical analysis, plotting, machine learning and big data analytics. Quantitative and qualitative evaluation with interviews outlines invaluable lessons learnt. Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 ethresearch Education ethresearch Data science ethresearch Cross-discipline ethresearch Big data ethresearch Research methodology ethresearch Learning ethresearch Constructivism theory ethresearch Transformative theory ethresearch Data Science Amsterdam : IOS Press 1 (1-2), pp. 101-117 http://hdl.handle.net/20.500.11850/172373 text/html WWW-Backlink auf das Repository (Open access) 1 Journal Article ethresearch ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/172373 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 100 1- Pournaras Evangelos ETHRESEARCH 773 0- Data Science Amsterdam : IOS Press 1 (1-2), pp. 101-117 BK010053 XK010053 XK010000 ETHRESEARCH ETHRESEARCH ETHRESEARCH Journal Article Open access