Sport Informatics and Analytics PG (9612.3)
Please note these are the 2022 details for this unit
Available teaching periods | Delivery mode | Location |
---|---|---|
View teaching periods | Online |
UC - Canberra, Bruce |
EFTSL | Credit points | Faculty |
0.125 | 3 | Faculty Of Health |
Discipline | Study level | HECS Bands |
Discipline Of Sport And Exercise Science | Post Graduate Level | Band 2 2021 (Commenced Before 1 Jan 2021) Band 4 2021 (Commenced After 1 Jan 2021) Band 4 2021 (Commenced After 1 Jan Social Work_Exclude 0905) |
In this unit, students will learn to develop, assess and deploy predictive models using machine learning techniques to support decision-making in a variety of sports settings. Students will gain critical insight into the analytic techniques used to describe the behaviour and performance of athletes and teams, and the systems used to present information gathered from sports data. Students will build their own interactive data presentation tools, such as dashboards and web-based applications.
1. Critically evaluate the analytic techniques and machine learning processes used to support decision-making in sport;
2. Employ concepts from machine learning to develop and assess predictive models using sports data;
3. Apply appropriate analytic techniques to gain critical insight into the behaviour and performance of athletes and teams; and
4. Develop an interactive data visualisation tool to present information and insights gathered from sports data.
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
Learning outcomes
On successful completion of this unit, students will be able to:1. Critically evaluate the analytic techniques and machine learning processes used to support decision-making in sport;
2. Employ concepts from machine learning to develop and assess predictive models using sports data;
3. Apply appropriate analytic techniques to gain critical insight into the behaviour and performance of athletes and teams; and
4. Develop an interactive data visualisation tool to present information and insights gathered from sports data.
Graduate attributes
1. UC graduates are professional - communicate effectively1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
Prerequisites
None.Corequisites
None.Incompatible units
None.Equivalent units
None.Assumed knowledge
It is assumed that students will have a functional knowledge of computer systems, allowing them to download and install software, browse the internet and access the course website. It assumed that students will have some experience using programming languages (such as R or Python) for data science. Some introductory programming material and resources will be provided.Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
---|---|---|---|---|---|
2022 | UC - Canberra, Bruce | Semester 2 | 01 August 2022 | Online | Dr Jocelyn Mara |
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit
timetable.