Treffer: Introductory Data Science across Disciplines, Using Python, Case Studies, and Industry Consulting Projects

Title:
Introductory Data Science across Disciplines, Using Python, Case Studies, and Industry Consulting Projects
Language:
English
Authors:
Lasser, Jana (ORCID 0000-0002-4274-4580), Manik, Debsankha, Silbersdorff, Alexander (ORCID 0000-0002-3453-9536), Säfken, Benjamin (ORCID 0000-0003-4702-3333), Kneib, Thomas (ORCID 0000-0003-3390-0972)
Source:
Teaching Statistics: An International Journal for Teachers. Sum 2021 43(1):S190-S200.
Availability:
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed:
Y
Page Count:
11
Publication Date:
2021
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
Geographic Terms:
DOI:
10.1111/test.12243
ISSN:
0141-982X
Entry Date:
2021
Accession Number:
EJ1299799
Database:
ERIC

Weitere Informationen

Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.

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