Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach
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| Title: | Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach |
|---|---|
| Language: | English |
| Authors: | Stuart King, Serveh Sharifi Far (ORCID |
| Source: | Teaching Statistics: An International Journal for Teachers. 2026 48(1):S67-S76. |
| 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: | 10 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Descriptive |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Data Science, Statistics Education, Teaching Methods, Interdisciplinary Approach, Introductory Courses, Graduate Students, Masters Programs, Blended Learning, Data Analysis, Skill Development, Programming Languages, Programming, Problem Solving, Ethics, Data Processing |
| DOI: | 10.1111/test.70014 |
| ISSN: | 0141-982X 1467-9639 |
| Abstract: | This paper describes the development and delivery of an introductory data science course designed for interdisciplinary master's students studying in a hybrid format, without advanced mathematics prerequisites. The course addresses the increasing need for data science skills among non-specialists, preparing them to tackle real-world challenges. It focuses on a hands-on, problem-led approach that emphasizes practical applications of data analysis and supports engagement among students from diverse academic backgrounds. The course teaches Python programming through Jupyter notebooks as the core tool for analyzing data and employs additional support tools to aid learning and assessment. This structure aims to build foundational data science skills and foster students' confidence in working with data. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1505713 |
| Database: | ERIC |
| Abstract: | This paper describes the development and delivery of an introductory data science course designed for interdisciplinary master's students studying in a hybrid format, without advanced mathematics prerequisites. The course addresses the increasing need for data science skills among non-specialists, preparing them to tackle real-world challenges. It focuses on a hands-on, problem-led approach that emphasizes practical applications of data analysis and supports engagement among students from diverse academic backgrounds. The course teaches Python programming through Jupyter notebooks as the core tool for analyzing data and employs additional support tools to aid learning and assessment. This structure aims to build foundational data science skills and foster students' confidence in working with data. |
|---|---|
| ISSN: | 0141-982X 1467-9639 |
| DOI: | 10.1111/test.70014 |
