Work

Teaching Data Science to Diverse Learners: A Hybrid and Interdisciplinary Approach

Year: 2025

Type: article

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.

Source: Teaching Statistics

DOI: 10.1111/test.70014

Authors: Stuart King, Serveh Sharifi Far

Institutions: Maxwell Institute for Mathematical Sciences, University of Edinburgh

Language: English

Topic: Statistics Education and Methodologies

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $2,900