Program Overview

Program Overview

The program in question appears to be related to a university course, specifically EXSC517 - Visual Optimisation in High Performance Sport. This unit is designed to provide students with evidence-based, ethically grounded, industry-relevant knowledge and skills in data handling and reporting, to effectively communicate ideas and outcomes to specialist and non-specialist stakeholders within the multiple sub-disciplines found in High Performance Sport settings.

Unit Rationale, Description, and Aim

The use of advanced techniques for data storage and visualization, to accurately interpret competition and training performance information, is essential when working in High Performance Sport. The unit addresses specialized coding and visualization principles for the analysis and interpretation of data in field and laboratory settings. The aim of the unit is to provide students with the necessary knowledge and skills to manage, analyze, and visualize data in High Performance Sport.

Learning Outcomes

To successfully complete this unit, students will be able to:

• Demonstrate advanced knowledge of data management and coding systems used for visualization of data and information in High Performance Sport.
• Perform specialized technical coding skills for summarizing, visualizing, and reporting data.
• Display and communicate data in ways appropriate to different audiences in High Performance Sport, demonstrating appropriate standards of ethical and technical conduct.

Content

The unit content includes:

• Visualizing single or multiple distributions
• Visualizing linear, non-linear univariate and multivariate relationships
• Visualizing amounts
• Visualizing proportions
• Visualizing variability and uncertainty
• Visualizing questionnaire response data
• Visualizing angles
• Visualization networks
• Visualizing qualitative data
• Visualizing paired comparisons
• Visualizing coordinate/spatial data
• Interactive visualization
• Illustrations
• Principle of visualization: color, shape, and composition theory

Assessment Strategy and Rationale

The assessment strategy in this unit has been designed to support learning as well as to assess it. It is sequenced so that the progression through the assessment matches the progression of learners through the learning outcomes. The assessments include:

• An examination to assess student learning of unit content
• An analysis task to assess student's ability to organize, analyze, and report data, and interpret its application to practice
• A written task to assess student's ability to analyze, report, and communicate data to industry-relevant audiences, displaying appropriate application of accumulated learning through the unit

Assessment Details

The assessments are as follows:

• Examination: 20% of the total grade, requires students to demonstrate their understanding and application of unit content
• Exploratory data visualization report: 40% of the total grade, requires students to demonstrate their application of knowledge and technical skills by conducting and documenting data exploration via visualization, and interpreting its application to practice
• Explanatory data visualization report: 40% of the total grade, requires students to demonstrate their application of knowledge and skills in analyzing and reporting data, and their ability for effective communication

Learning and Teaching Strategy and Rationale

The learning and teaching strategy in this unit has been designed to support learning in the online environment, to meet the aim, learning outcomes, and graduate attributes of the unit, and reflect respect for the individual as an independent learner. A range of approaches are utilized, so that the unit's content and activities progress students through the learning outcomes and associated assessment tasks.

Representative Texts and References

The unit references several key texts, including:

• Ozgur, C., Colliau, T., Rogers, G., and Hughes, Z. (2017). MatLab vs. Python vs. R. Journal of Data Science, 15(3), pp.355-371
• Rivas, P. (2020). Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python. Packt Publishing Ltd
• Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. 2nd ed. New York: Springer
• Wilke, C. (2020). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. 1st ed. CA: O'Reilly Media

Unit Details

• Credit points: 10
• Year: 2025
• Location: Online
• Term: ACU Term 1 and ACU Term 3
• Mode: Online Unscheduled
• Prerequisites: Nil
• Incompatible units: EXSC514 Sports Analytics and Visualisation