Applied Data Analysis in Sport PG (10157.2)
|Available teaching periods||Delivery mode||Location|
|View teaching periods|| Online
|| UC - Canberra, Bruce
|0.125||3||Faculty Of Health|
|Discipline||Study level||HECS Bands|
|Discipline Of Sport And Exercise Science||Post Graduate Level|| Band 1 2013-2020 (Expires 31 Dec 2020)
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 outcomesOn successful completion of this unit, students will be able to:
1. Critically analyse and interpret sports data using the data analysis program R;
2. Effectively visualise and communicate data to a variety of audiences in sport; and
3. Efficiently manage and transform sports data using data science techniques.
Graduate attributes1. 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 - 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
Assumed knowledgeBasic word processing and spreadsheet capabilities.
|Year||Location||Teaching period||Teaching start date||Delivery mode||Unit convener|
|2021||UC - Canberra, Bruce||Semester 1||08 February 2021||Online||Dr Jocelyn Mara|
|2022||UC - Canberra, Bruce||Semester 1||07 February 2022||Online||Dr Jocelyn Mara|
Weekly readings and exercises will be specified on the unit site and will be sourced from a variety of open (free!) online e-books/other resources. These online books include:
- R for Data Science, by Garrett Grolemund and Hadley Wickham https://r4ds.had.co.nz
- Advanced R, by Hadley Wickham https://adv-r.hadley.nz
- Introduction to Data Science, by Rafael A. Irizarry https://rafalab.github.io/dsbook/
- 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 https://whattheyforgot.org
- Hands on Programming with R, Garrett Grolemund https://rstudio-education.github.io/hopr/
This is a 100% online unit and therefore there are no face-to-face requirements. However, it is expected that students will engage with the online learning material, and participate in discussion forums.
Students are strongly encouraged to attend the optional student drop-in times on Wednesdays at 9.30am-10.30am. These will be conducted in the Virtual Room and will provide an opportunity for students to ask questions and discuss the unit content and assessment items. These will also be an excellent opportunity for students to meet and network with others enrolled in the unit.
Required IT skills
This unit will use the open source data analysis software 'RStudio'. A basic understanding of data software (e.g. Microsoft Excel) is assumed, however it is not assumed students have used RStudio before. Therefore, tutorials in R programming will be provided throughout the semester.
Work placement, internships or practicums
CONTACTING THE UNIT CONVENOR
Where possible, students with general questions about the unit content and assessment should use the discussion forum's 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.
Students are also strongly encouraged to utilise the student drop-in times on Wednesdays at 9.30am - 10.30am. These optional sessions will be conducted in the Virtual Room on the unit site.
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.