spe2dss data science and statistics for sport
DATA SCIENCE AND STATISTICS FOR SPORT
SPE2DSS
2019
Credit points: 15
Subject outline
Students will develop an understanding of research methodology, statistical analysis and data science techniques specific to the application within sports and exercise science. Students will explore various research designs and statistical approaches that will allow them to critically analyse data to apply results in a practical environment. The subject will teach students how to perform various data handling techniques, statistical tests and explore data visualisation techniques to effectively demonstrate outcomes in a professional setting.
SchoolSchool Allied Health,Human Serv & Sport
Credit points15
Subject Co-ordinatorAndrew Govus
Available to Study Abroad StudentsYes
Subject year levelYear Level 2 - UG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites HLT1RAE
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Statistics for sports and exercise science: a practical approach | Prescribed | Newell, J., Aitchison, T., & Grant, S. (2014) | Routledge |
Graduate capabilities & intended learning outcomes
01. Demonstrate an understanding of data types and research designs that are applicable to sports and exercise science.
- Activities:
- Workshops, Online learning modules, Lectures
02. Apply data handling techniques to correctly treat and process data to provide a range of descriptive statistical outcomes.
- Activities:
- Workshops, Online learning modules, Lectures
03. Select, apply and interpret various statistical tests to provide evidence-based recommendations.
- Activities:
- Workshops, Online learning modules, Lectures
04. Critically appraise statistical designs and research literature within sports and exercise science.
- Activities:
- Workshops, Online learning modules, Lectures
05. Apply a range of data visualization techniques to demonstrate meaningful outcomes.
- Activities:
- Workshops, Online learning modules, Lectures
Subject options
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Melbourne, 2019, Semester 1, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorAndrew Govus
Class requirements
LectureWeek: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via blended.
TutorialWeek: 10 - 22
One 2.0 hours tutorial per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| 5 x Online Quizzes - 10 questions each (750 words total) | 20 | 01, 04 | |
| Tutorial Workbook (1500 words) | 40 | 01, 02, 03, 05 | |
| Assignment (equivalent to 1500 words) | 40 | 01, 02, 03, 04, 05 |