Data science course design for a large-scale cohort using individual project-based learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Being an effective data scientist includes mastering many skills, both technical and analytical. There are many great teaching resources for learning technical skills. However, the analytical skills of understanding customer values, proposing causal relationships and gathering datasets are less common. This paper describes a new data science course designed to emphasize these analytical skills using individual project-based learning (PBL). PBL is considered to be a valuable teaching approach in computer science education. However, such courses typically have large-scale cohorts, resulting in PBL being used as group work. Our approach using individual PBL, circumvents issues concerning team dynamics and individual student assessment within group work. This course is designed to work for a large-scale (75 +) postgraduate cohort of both full- and part-time students. To facilitate the large-scale cohort the course makes use of a virtual learning environment (VLE) and recorded lectures. Students were able to choose the subject of their project and what software they would use to create visualizations. This paper provides details of a novel approach to teaching data science using individual PBL for a large-scale cohort while maintaining education quality.

Original languageEnglish
Title of host publicationProceedings of the 7th Conference in Computing Education Practice, CEP'23
EditorsEleni Akrida, Mark Zarb
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450398213
Publication statusPublished - 06 Jan 2023
Event7th Computing Education Practice Conference - Durham, United Kingdom
Duration: 06 Jan 202306 Jan 2023

Publication series

NameComputing Education Practice


Conference7th Computing Education Practice Conference
Abbreviated titleCEP
Country/TerritoryUnited Kingdom
Internet address


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