Athlete Monitoring PG (10156.3)
|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 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 evaluate different methods and systems used to monitor the dose-response relationship in athletes;
2. Apply advanced knowledge of evidence-based principles of athlete monitoring;
3. Manage and analyse athlete monitoring data using a programming environment; and
4. Interpret and effectively communicate information gathered from athlete monitoring data.
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 - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - communicate effectively in diverse cultural and social settings
Assumed knowledgeIt 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|
|2022||UC - Canberra, Bruce||Semester 1||07 February 2022||Online||Dr Jocelyn Mara|
There are no required texts for this unit, however students might be interested in the following optional text:
In addition, students may also be interested in these free online books to assist in learning the programming language R, and Integrated Development Environment (IDE) RStudio, which will be used throughout the unit:
- 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/
Required readings and other complementary learning resources will be available on the unit Canvas site.
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.
It is also strongly encouraged that students attend the optional drop-in sessions. These will be conducted on Microsoft Teams and provide an opportunity for students to ask questions and discuss the unit content and assessment items. It is also a good opportunity for students to meet others enrolled in the unit.
Required IT skills
It is expected that students will have UC entry level IT skills, allowing them to access the unit Canvas site. It is assumed that students have a reasonable understanding of Microsoft Office software (or equivalent). In addition, 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.
It is also strongly encouraged that students attend the optional drop-in sessions. These will provide an opportunity for students to ask questions and discuss the unit content and assessment items. It is also a good opportunity for students to meet others enrolled in the unit.
If students do need to send an email:
- The email must be sent from the student's UC email account. Correpondence from personal email addresses can not be responded to.
- The subject line must include the unit code and context of the message (e.g. 10156 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.