| Effective Term: | 2024/05 |
| Institute / School : | Institute of Health and Wellbeing |
| Unit Title: | Data Analytics for Sport Management |
| Unit ID: | ISMAN2006 |
| Credit Points: | 15.00 |
| Prerequisite(s): | Nil |
| Co-requisite(s): | Nil |
| Exclusion(s): | Nil |
| ASCED: | 080399 |
| Other Change: | |
| Brief description of the Unit |
This unit introduces students to the application of an analytics framework for sport organisations. The unit aims to provide students with knowledge and skill to perform data analysis utilising real world data to reveal patterns of behaviour and trends to inform decision makers in sport management. The content includes; data analytic frameworks and data mining; the gathering and preparation of data; data visualisation and effective communication of data analytics to key stakeholders. The context of data will focus on sport participation, customer relationship management, sport events, sport facilities and fan engagement. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: | |
| Supplementary Assessment:No |
| Supplementary assessment is not available to students who gain a fail in this Unit. |
| Course Level: |
| Level of Unit in Course | AQF Level(s) of Course | | 5 | 6 | 7 | 8 | 9 | 10 | | Introductory | | | | | | | | Intermediate | | | | | | | | Advanced | | |  | | | |
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| Learning Outcomes: |
| Knowledge: |
| K1. | Identify and describe the utilisation and benefits of data analysis in sport management practice. |
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| K2. | Describe the data analytics framework and explain the major stages of data analysis. |
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| K3. | Classify data by type including text, numeric, date/time and geographic data types, including identifying the differences between numerical and categorical, discrete and continuous, and structured and unstructured data. |
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| K4. | Describe and explain privacy, security and ethical considerations when collecting, analysing and presenting data. |
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| Skills: |
| S1. | Prepare data for visual analysis by cleaning, validating and formatting data sources. |
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| S2. | Compile and develop visual analytics and dashboards using business intelligence software. |
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| S3. | Critically evaluate information using data analytics techniques to inform decision making in developing innovative recommendations, plans and strategies. |
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| Application of knowledge and skills: |
| A1. | Compile, clean and organise real-life data in preparation for performing a data analysis. |
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| A2. | Design and develop a data dashboard to deliver visual analytics to inform trends and strategic decision making. |
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| A3. | Develop and present an analysis of real-life data for a sport organisation providing insights, recommendations and identifying data assumptions and limitations. |
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| Unit Content: |
This may include:
• Data analytics in sport management
• Collecting data for sport management
• Preparing sports data for analysis
• Sports participation analytics
• Analytics for Customer Relationship Management (CRM)
• Event analytics
• Facility analytics
• Engaging with fans
• Effective communication of sports data and analysis |
| Graduate Attributes: |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | | 1. | K1, K2, K3, K4, S3 | Multiple choice and short-answer questions to demonstrate continuous understanding of unit content | Online quizzes | 10-20% | | 2. | K3, S1, A1 | Compile, clean and organise data for performing a data analysis | Individual lab file and report | 15-30% | | 3. | K3, S1, S2, A2 | Design and develop a data dashboard to present visual analytics and facilitate interpretation of data | Individual lab file and report | 20-30% | | 4. | K3, K4, S1, S2, S3, A3 | Develop and present a sport management analysis of a sport organisation, providing insights and recommendations and identifying data assumptions and limitations | Individual report and video presentation | 30-50% |
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