Sport Informatics and Analytics PG (9612.3)
| Available teaching periods | Delivery mode | Location |
|---|---|---|
| View teaching periods | Online |
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) |
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 | Bruce, Canberra | Semester 2 | 31 July 2023 | Online | Dr Jocelyn Mara |
| 2024 | Bruce, Canberra | Semester 2 | 29 July 2024 | Online | Dr Jocelyn Mara |
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 and URKUND). 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.
Academic integrity
Students have a responsibility to uphold University standards on ethical scholarship. Good scholarship involves building on the work of others and use of others' work must be acknowledged with proper attribution made. Cheating, plagiarism, and falsification of data are dishonest practices that contravene academic values. Refer to the University's Student Charter for more information.
To enhance understanding of academic integrity, all students are expected to complete the Academic Integrity Module (AIM) at least once during their course of study. You can access this module within UCLearn (Canvas) through the 'Academic Integrity and Avoiding Plagiarism' link in the Study Help site.
Use of Text-Matching Software
The University of Canberra uses text-matching software to help students and staff reduce plagiarism and improve understanding of academic integrity. The software matches submitted text in student assignments against material from various sources: the internet, published books and journals, and previously submitted student texts.
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, and join a Microsoft Teams meeting. 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 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.
