Sport Informatics and Analytics PG (9612.3)
| 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) |
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 - 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.| Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
|---|---|---|---|---|---|
| 2023 | UC - Canberra, Bruce | Semester 2 | 31 July 2023 | Online | Dr Jocelyn Mara |
Required texts
There are no required texts for this unit. However recommended readings for each week will be provided on the unit's Canvas site.
Participation requirements
This unit will be taught completely online, and while there are no live or face-to-face lessons, it is expected that students will 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 the Canvas site. It is also expected that students have an understanding of how to download and install software on their computer. It is assumed that students will have some familiarity or experience using programming languages (such as R or Python), however introductory material and resources for R will be provided on Canvas.
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 on Wednesdays at 11.30am-12.30pm. Students can book in to a consultation time using the Calendar on Canvas, and the consultation will be conducted via Microsoft Teamsor face-to-face in 12C3b.
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.
