Applied Data Analysis in Sport PG (10157.3)
| 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) |
Learning outcomes
On successful completion of this unit, students will be able to:1. Apply advanced knowledge of best-practice programming principles and data science techniques;
2. Access, manage and transform sports data from a variety of sources;
3. Develop statistical models to answer questions from a range of sports settings;
4. Effectively communicate information gathered from sports data; and
5. Generate reproducible data analysis reports and demonstrate best-practice literate programming skills.
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 - communicate effectively in diverse cultural and social settings
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
2. UC graduates are global citizens - think globally about issues in their profession
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 - evaluate and adopt new technology
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.| Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
|---|---|---|---|---|---|
| 2024 | Bruce, Canberra | Semester 1 | 05 February 2024 | Online | Dr Jocelyn Mara |
| 2025 | Bruce, Canberra | Semester 1 | 03 February 2025 | Online real-time | Dr Jocelyn Mara |
Required texts
Weekly readings will be provided on the unit Canvas site and will be sourced from a variety of open (free!) e-books and other online resources. These online books include:
- R for Data Science, by Hadley Wickham, Mine Cetinkaya-Rundel & Garrett Grolemund https://r4ds.hadley.nz/
- Advanced R, by Hadley Wickham https://adv-r.hadley.nz/
- Introduction to Data Science, by Rafael A. Irizarry http://rafalab.dfci.harvard.edu/dsbook-part-1/
- Advanced Data Science, by Rafael A. Irizarry http://rafalab.dfci.harvard.edu/dsbook-part-2/
- R Programming for Data Science, by Roger Peng https://bookdown.org/rdpeng/rprogdatascience
- Exploratory Data Analysis, by Roger Peng https://bookdown.org/rdpeng/exdata
- What they Forgot to Teach you About R, by Jennifer Bryan, Jim Hester, Shannon Pileggi & E.David Aja https://rstats.wtf
- Hands on Programming with R, Garrett Grolemund https://rstudio-education.github.io/hopr/
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 is delivered online and while there are no on-campus requirements, it is expected that students will attend and actively engage in the synchronous online tutorials. It is also expected that students will engage with the asynchronous learning material, and participate in discussion forums.
Required IT skills
This unit will use the open source statistical and programming language R and the integrated development environment (IDE) RStudio. An understanding of and previous experience with data analysis software (e.g. Microsoft Excel) is assumed, however it is not assumed students have used R or RStudio before.
Work placement, internships or practicums
None.
Additional information
CONTACTING THE UNIT CONVENOR
Students with questions about the unit content and assessment should use the discussion forum on the unit Canvas site. Students that post to the discussion forum should expect a reply from the unit convenor within 1 business day.
If students do need to send an email:
- The email must be sent from the student's UC email account. Correpondance from personal email addresses can not be responded to.
- The subject line must include the unit code and context of the message (e.g. 10157 Assessment 1)
- The student must address the unit convenor appropriately by name
- The question or request should be stated clearly and concisely
- The student should sign off with their full name and student ID number.
Emails that don't comply with these conventions will not receive a reply. Students can expect a response via email within 2-3 business days.
