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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. https://doi.org/10.3233/DS-170005 http://purl.org/dc/terms/date 2017-10-17 https://doi.org/10.3233/DS-170005 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2451-8492 https://doi.org/10.3233/DS-170005 http://purl.org/dc/terms/title Cross-disciplinary higher education of data science – beyond the computer science student https://doi.org/10.3233/DS-170005 http://purl.org/ontology/bibo/issue 1-2 https://doi.org/10.3233/DS-170005 http://purl.org/ontology/bibo/volume 1 https://doi.org/10.3233/DS-170005 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/PositionPaper https://orcid.org/0000-0003-3900-2057 http://schema.org/affiliation https://ror.org/05a28rw58 https://orcid.org/0000-0003-3900-2057 http://schema.org/email epournaras@ethz.ch https://orcid.org/0000-0003-3900-2057 http://xmlns.com/foaf/0.1/name Evangelos Pournaras https://ror.org/05a28rw58 http://xmlns.com/foaf/0.1/name Professorship of Computational Social Science, ETH Zurich, Clausiusstrasse 50, 8092, Zurich, Switzerland https://w3id.org/kpxl/ios/ds/np/RAiLuFO4hWLoPyvVn2mSIJJ38Yu5hq7qgLMFJdvXeMEQk/author-list http://www.w3.org/1999/02/22-rdf-syntax-ns#_1 https://orcid.org/0000-0003-3900-2057 https://w3id.org/kpxl/ios/ds/np/RAiLuFO4hWLoPyvVn2mSIJJ38Yu5hq7qgLMFJdvXeMEQk/provenance https://w3id.org/kpxl/ios/ds/np/RAiLuFO4hWLoPyvVn2mSIJJ38Yu5hq7qgLMFJdvXeMEQk/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0000-0003-3900-2057 https://w3id.org/kpxl/ios/ds/np/RAiLuFO4hWLoPyvVn2mSIJJ38Yu5hq7qgLMFJdvXeMEQk/pubinfo https://orcid.org/0000-0002-1267-0234 http://xmlns.com/foaf/0.1/name Tobias Kuhn https://orcid.org/0000-0003-3900-2057 http://xmlns.com/foaf/0.1/name Evangelos Pournaras https://w3id.org/kpxl/ios/ds/np/RAiLuFO4hWLoPyvVn2mSIJJ38Yu5hq7qgLMFJdvXeMEQk http://purl.org/dc/terms/created 2025-05-05T09:15:59.143Z 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