Cross-disciplinary Higher Education of Data Science-Beyond the Computer Science Student
| LEADER | naa a22 4500 | ||
|---|---|---|---|
| 001 | 528782223 | ||
| 005 | 20180924065513.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 180924e201712 xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.3929/ethz-b-000172373 |2 doi |
| 024 | 7 | 0 | |a 10.3233/DS-170005 |2 doi |
| 035 | |a (ETHRESEARCH)oai:www.research-collecti.ethz.ch:20.500.11850/172373 | ||
| 100 | 1 | |a Pournaras |D Evangelos | |
| 245 | 1 | 0 | |a Cross-disciplinary Higher Education of Data Science-Beyond the Computer Science Student |h [Elektronische Daten] |c [Evangelos Pournaras] |
| 506 | |a Open access |2 ethresearch | ||
| 520 | 3 | |a 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. | |
| 540 | |a Creative Commons Attribution 4.0 International |u http://creativecommons.org/licenses/by/4.0 |2 ethresearch | ||
| 690 | 7 | |a Education |2 ethresearch | |
| 690 | 7 | |a Data science |2 ethresearch | |
| 690 | 7 | |a Cross-discipline |2 ethresearch | |
| 690 | 7 | |a Big data |2 ethresearch | |
| 690 | 7 | |a Research methodology |2 ethresearch | |
| 690 | 7 | |a Learning |2 ethresearch | |
| 690 | 7 | |a Constructivism theory |2 ethresearch | |
| 690 | 7 | |a Transformative theory |2 ethresearch | |
| 773 | 0 | |t Data Science |d Amsterdam : IOS Press |g 1 (1-2), pp. 101-117 | |
| 856 | 4 | 0 | |u http://hdl.handle.net/20.500.11850/172373 |q text/html |z WWW-Backlink auf das Repository (Open access) |
| 908 | |D 1 |a Journal Article |2 ethresearch | ||
| 950 | |B ETHRESEARCH |P 856 |E 40 |u http://hdl.handle.net/20.500.11850/172373 |q text/html |z WWW-Backlink auf das Repository (Open access) | ||
| 950 | |B ETHRESEARCH |P 100 |E 1- |a Pournaras |D Evangelos | ||
| 950 | |B ETHRESEARCH |P 773 |E 0- |t Data Science |d Amsterdam : IOS Press |g 1 (1-2), pp. 101-117 | ||
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B ETHRESEARCH |F ETHRESEARCH |b ETHRESEARCH |j Journal Article |c Open access | ||