Sport Informatics and Analytics PG (9612.3)

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.

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 effectively
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

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.