Postgraduate Course: Data, Sport & Society (STIS11002)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | From the boardroom to the touchline, data analytics is a growing area of importance within sport and this course is designed for learners with no prior experience in computer programming, who would like to gain some understanding of this area.
The course has two main components: a taught element on real-life applications of data analytics in sport and an entry-level tutorial on sport event data analysis using the programming language Python. This practical case study in data science application is also relevant for students who do not have an initial interest for sport analytics, to gain applied skills that might translate into a vocational setting.
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| Course description |
The course is about being able to develop sport analytics as well as critically assessing its wider social and epistemological implications. In the first part of the course, students learn transferrable skills in data analysis to build their own project. While in the second, they learn the critical tools of the sociology of science to be able to assess and communicate the results to a non-data literate audience of sport industry stakeholders. Initially, students will learn how to explore a sport dataset to then familiarise with tasks related to sorting events and creating categories in a sport dataset. How to use Python libraries to run statistical analyses of sport data will be a core learning in the course, together with how to build event chains from a sport dataset. After the mid-course project presentation, students will be introduced to elements of the genealogy of sport data, leading to a critique of AI applications in sport. Sport insider views on the use of algorithms will be discussed to introduce learners to controversies related to the often-contrasting evidence of eye test vis-à-vis data insights when it comes to assess sporting performance. The course will conclude with recommendations on how to better connect visual and numerical evidence.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 30,
Dissertation/Project Supervision Hours 10,
Summative Assessment Hours 60,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
86 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Notebook, 40%
Short essay, 60% |
| Feedback |
Feedback will be given to a draft notebook in Week 5. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply knowledge, skills and understanding in using data analytics applied to sport data
- Demonstrate and work with a critical understanding of the wider social and epistemological implications of these applications
- Manage complex professional issues not addressed by the current work environment for data professionals in the sport industry
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Reading List
Collins H. (2010), The Philosophy of Umpiring and the Introduction of Decision- Aid Technology, in Journal of the Philosophy of Sport, 37, 2, pp. 135-46.
Mulvin D. (2014), Game Time: A History of the Managerial Authority of the In- stant Replay, in T. Oates, Z. Furness (eds.), The nfl: Critical and Cultural Perspectives, Temple University Press, Philadelphia, pp. 40-59.
Staley, K., V. (1999) Golden Events and Statistics: Whats Wrong with Galisons Image/Logic Distinction?, Perspectives on Science, 7(2): 196-230.
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Additional Information
| Graduate Attributes and Skills |
The course will develop skills to create, identify and evaluate options in order to solve complex problems. It will also develop students' digital literacy including familiarity with computer programming and ability to manipulate numbers and its application in practical contexts. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Gian Campagnolo
Tel: (0131 6)51 4273
Email: g.campagnolo@ed.ac.uk |
Course secretary | Ms Maria Brichs
Tel: (0131 6)51 3205
Email: mbrichs@ed.ac.uk |
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