Sport Informatics and Analytics PG (9612.3)

Please note these are the 2025 details for this unit
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Sport Informatics and Analytics PG
Available teaching periods Delivery mode Location
View teaching periods Online
Online real-time
 Bruce, Canberra
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.

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 - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
3. UC graduates are lifelong learners - be self-aware
3. UC graduates are lifelong learners - evaluate and adopt new technology


Skills development

 

 


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.
Availability for enrolment in 2025 is subject to change and may not be confirmed until closer to the teaching start date.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2024 Bruce, Canberra Semester 2 29 July 2024 Online Dr Jocelyn Mara
2025 Bruce, Canberra Semester 2 28 July 2025 Online real-time 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.

Required texts

There are no required text-books for this unit. Required readings will be provided on Canvas under the unit Reading List. Other recommended resources will also be provided on Canvas, within the relevant module pages.

Submission of assessment items

Extensions & Late submissions

For clarification, one (1) minute past the specified due date and time is considered a late submission.

It is students' responsibility to be familiar with the electronic submission process (eg. the use of Canvas). Students are reminded to ensure they plan well ahead by enabling adequate time to submit assessments prior to the deadline. 

Quizzes

Students who do not attempt the quiz by the due date and time will forfeit any marks and receive zero for that quiz. Any quizzes in progress at the due time will be automatically submitted. Students should ensure they begin their quiz attempt with sufficient time to complete it prior to the due time. If you are concerned about the stability and reliability of your internet connection, it is recommended you use a student computer in the library when completing quizzes.

Moderation of assessments

Please note that all assessments will be moderated as outlined in the Moderation Policy found on the Canvas page.

Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Participation requirements

This unit will be taught completely online, and it is expected that students will attend the online live workshops, and actively engage with the recorded lessons, the practical coding activities, required readings, discussion forums and other provided resources to get the most out of this unit.

Required IT skills

It is expected that students will possess UC IT entry skills, allowing them to access and use the Canvas site. It is also assumed that students have an understanding of how to download and install software onto their computer. It is assumed students have a good understanding of data analysis software (e.g. Microsoft Excel), video presentation software (e.g. Microsoft Powerpoint) and word processing software (e.g. Microsoft Word). Introductory material and resources for conducting the relevant data analyses in RStudio will be provided on the Canvas site.   

Work placement, internships or practicums

None. 

Additional information

 

Contacting the unit convenor

 

Discussion forums:

Where possible, students with general questions about unit content and assessment should use the discussion forums on the unit Canvas site. Students that post to the discussion forum should expect a reply or confirmation from the unit convenor within 1 business day.

 

Student consultations:

Students that have more specific questions about the unit content and assessments should utilise the student consultation times. Students can book in to a consultation time using the Calendar on Canvas, and the consultation will be conducted via the Virtual Room or face-to-face in 12C13.

 

Email:

If you do need to send an email:

  • The email must be sent from your student email account. Correspondance from personal email addresses can not be responded to.
  • The subject line must include the unit code and context of your message (e.g. 9612 Assignment 1)
  • Address the unit convenor appropriately by name
  • State your question or request clearly and concisely
  • Sign off with your full name and student ID number

Emails that don't conform with these conventions will not receive a reply. Students can expect a response via email within 2 business days.

 

Next step

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