92526 Data Management in Sport and Exercise
The Complete Exam Bible · Spring B 202692526 Overview
- University of Technology Sydney
- Spring B 2026
- Sport and exercise data
- Blended Canvas modules and tutorials
- Microsoft Excel tutorials
Data Management in Sport and Exercise is a University of Technology Sydney subject for students in the sport and exercise degrees, and its homepage describes it as equipping you to handle data across sport and exercise contexts, whatever your stream.
- Assessed by A paired data management plan presentation (30%), a working-with-data task (40%) and a final exam (30%) in the examination period.
- Hardest step Applying a framework such as the data lifecycle or the Capability Maturity Model to one specific sport scenario, stage by stage, instead of listing its terms.
- Do first Rebuild each Excel tutorial from a blank workbook: linked functions, absolute references, pivot tables, histograms and boxplots.
What you get in the full 92526 Exam Bible
- 14 chapters, 60 pages — every examinable topic, week by week
- 34 original practice questions, each with a model answer in the Assessment task 3 format
- 14 worked examples, solved the way 92526 marks
- 19 computed figures, redrawn from the course’s own numbers
- Exam-morning plan for the Assessment task 3 (30%) — what to revise last, where marks are lost
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full exam bible.
How 92526 is assessed
| Component | Weight | Format |
|---|---|---|
| Assessment task 1: Data as an asset | 30% | In pairs: a data management plan for a sports organisation you invent, delivered as a pre-recorded 10-minute presentation with a CRediT author statement and references; due 13 September 2026, 23:59 (Week 7). |
| Assessment task 2: Working with data | 40% | Due 1 November 2026 (Week 11); the task description is on the Canvas Assignments page. |
| Assessment task 3: Final exam | 30% | Held in the examination period; eligibility also depends on meeting the Sport and Exercise Science attendance framework. |
The three weights come from the subject's assessment overview on Canvas, which names the subject outline as the definitive source if details differ. Due dates come from the subject schedule and the Assessment 1 page. Under the Sport and Exercise Science attendance framework, three or more absences that do not meet the Special Consideration criteria make a student ineligible for the final assessment task, with a Fail grade awarded.
Assessment structure
What other students are asking about 92526
- Where can I find lecture notes or study guides for 92526 at UTS?
- The 92526 study guide in AskSia Library covers Data Management in Sport and Exercise in 14 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in 92526, and how much is it worth?
- Yes. In the offering this AskSia Library guide is built from, Assessment task 3: Final exam carries 30% of the 92526 mark; the full split is Assessment task 1: Data as an asset 30%, Assessment task 2: Working with data 40%, Assessment task 3: Final exam 30%.
- What does 92526 cover on where data comes from across sport?
- In AskSia Library's 92526 guide, the Where Data Comes From Across Sport chapter explains biomechanics, physiology and performance analysis sources · medicine, psychology and nutrition · business, talent and marketing data.
- What does 92526 cover on the data lifecycle from origin to reflection?
- In AskSia Library's 92526 guide, the The Data Lifecycle from Origin to Reflection chapter explains origin, identification, capture and transfer · storage and processing · analysis, dissemination and reflection.
- What does 92526 cover on data types and how computers store them?
- In AskSia Library's 92526 guide, the Data Types and How Computers Store Them chapter explains nominal and ordinal · discrete and continuous · interval and ratio · integers, floats, strings and booleans · structured data.
Current 92526 dates
| Date | Item | Control |
|---|---|---|
| 13 September 2026, 23:59 | Assessment 1: Data as an asset due | Week 7; pre-recorded presentation, author statement and references. |
| 21 to 27 September 2026 | Stuvac | Followed by two alternate learning weeks before Week 9. |
| 1 November 2026 | Assessment 2: Working with data due | Week 11. |
| 9 to 15 November 2026 | Final Stuvac | The final exam is held in the examination period. |
Dates are as published in the Spring B 2026 subject schedule on Canvas and the Assessment 1 page. Confirm exact deadlines and submission settings in the live LMS.
What 92526 covers
The subject builds in five steps across Weeks 1 to 5: what data is, where sport finds it and how one data source moves through its lifecycle; data types, tidy organisation and whether data is meaningful; summarising and graphing categorical and continuous data in Excel; computational thinking and communicating with computers; and the governance, law and ethics of athlete data.
Defining Data and the DIKW Pyramid
data units, data items and observations · data, information, knowledge and wisdom · why data is messy and socially constructedThe first module sets the vocabulary the rest of the subject depends on.
- The ABS definition of data
- Data units, data items and observations
- Counting observations in a sports table
- Data, information, knowledge and wisdom
- Why data is messy, incomplete and socially constructed
- Explore, trust and predict as reasons to collect
Where Data Comes From Across Sport
biomechanics, physiology and performance analysis sources · medicine, psychology and nutrition · business, talent and marketing dataData in sport is everywhere, and this chapter makes that claim specific.
- Areas of sport that collect data
- Biomechanics, physiology and strength testing sources
- Performance analysis, tracking and video
- Medicine, psychology and nutrition data
- Business, talent identification and marketing data
- Capture modes and their quality risks
The Data Lifecycle from Origin to Reflection
origin, identification, capture and transfer · storage and processing · analysis, dissemination and reflectionThe data lifecycle is the Week 1 framework for getting data that can genuinely influence a decision. It follows one data source through nine stages: origin, identification, capture, transfer, storage, processing, analysis, dissemination and reflection.
- Origin and data identification
- Capture, transfer and storage
- Processing and creating new variables
- Analysis and dissemination
- Reflection as the step that closes the loop
- Requirements, risks and constraints at each stage
Data Types and How Computers Store Them
nominal and ordinal · discrete and continuous · interval and ratio · integers, floats, strings and booleans · structured dataWeek 2 asks what forms data can take before anything is summarised, and the answer is a short hierarchy that decides most later choices.
- Categorical and numerical data
- Nominal and ordinal categories
- Discrete and continuous measures
- Interval and ratio scales
- Integers, floats, strings and booleans
- Structured and unstructured data
Organising Spreadsheets as Tidy Data
tidy data · common layout issues · tips for spreadsheets · data dictionaries · filter, transform, aggregate and sortA spreadsheet can look neat and still be unusable for analysis.
- The tidy-data definition
- Five common layout issues
- Ten tips for organising spreadsheets
- ISO 8601 dates and missing-value codes
- Data dictionaries and expected ranges
- Data validation and plain-text copies
Meaningful Data: Quality, Questions and Bias
data quality dimensions · open and closed questions · Patton's question types · bias in collectionWell-organised data can still be meaningless, and this chapter covers the Week 2 tools for telling the difference.
- Why collection methods matter as much as data
- Open and closed question forms
- Patton's question types
- Completeness, uniqueness and validity
- Timeliness, accuracy and consistency
- Fitness for purpose
Top-Down and Bottom-Up Analysis
problem-first planning · pooling data across systems · strengths and risks of each routeThe final Week 2 video, Thinking forwards, asks how an analysis should begin: with the problem or with the data already held.
- The top-down route from problem to answer
- The bottom-up route from pooled data to insight
- Prospective collection and pre-specified models
- Pooling and mining data across systems
- Strengths and risks of each route
- Latent variables and controlled sampling
Summarising and Graphing Categorical Data
sample and population proportions · pie charts and their limits · bar charts from a zero baselineWeek 3 begins with categorical data, which is summarised by counting each category and converting the counts to proportions.
- Counting categories and computing proportions
- Population and sample proportions
- Frequency tables and slice angles
- What pie charts do well and badly
- Bar charts from a zero baseline
- Truncated axes that exaggerate differences
Histograms, Boxplots and Continuous Summaries
bins and bin width · skew and shape · mean, median and mode · IQR and boxplot fences · scatterplots and line graphsContinuous data is the more common kind in sport and needs extra steps before it can be pictured.
- Grouping continuous values into bins
- Bin width from range and number of bins
- Histogram shapes: symmetric, skewed, bimodal
- Mean, median and mode
- Variance, IQR and range
- Five-number summary and boxplot fences
Choosing Graphs for Fast Perception
comparison, correlation, distribution, part-to-whole and time charts · Gestalt principles · ranking of visual encodingsThe Week 3 material asks what makes a graph good, beyond picking a template.
- Why visualisations speed up understanding
- Ranking of visual encodings
- Comparison and correlation charts
- Distribution, part-to-whole and time charts
- Proximity and similarity in subplots
- Common region and common fate
Excel Functions, Referencing and Pivot Tables
linked cells and built-in functions · BMI formula · absolute references · ROUNDDOWN · sort, filter and pivot tablesThe tutorials of Weeks 2 to 4 turn the subject's organising principles into Excel skills, and this chapter gathers the ones that carry calculation.
- Linked cells and built-in functions
- LARGE, COUNTIF and nested functions
- Writing a BMI formula in Excel syntax
- Relative versus absolute references
- Why manual block averages break on re-sort
- Pivot table filters, rows, columns and values
Computational Thinking and Talking to Computers
decomposition, abstraction, pattern recognition and algorithms · R and Python versus Excel · prompting AI toolsWeek 4 asks how to represent a data problem so that a computer, or any software, can act on it.
- Decomposition and abstraction
- Pattern recognition and algorithms
- Problem formulation, expression and evaluation
- Applying the phases to a sport problem
- R and Python compared with Excel
- The RStudio window
Data Governance Components and Maturity
stewardship, quality, policies, security, compliance, architecture and lifecycle management · Capability Maturity Model levelsWeek 5 moves from handling data to governing it.
- What data governance covers
- Data stewardship and data quality
- Policies, standards and security
- Compliance, architecture and lifecycle management
- The five Capability Maturity Model levels
- Rating an organisation with evidence
Athlete Data: Privacy Law, Ownership and Ethics
athlete data categories · Privacy Act 1988 and consent · data ownership · athlete-centric governance · ethical risksThe second half of Week 5 looks at data from the athlete's side.
- Geospatial, biomechanical, physiological and wellbeing data
- Collection beyond what athletes need
- The Privacy Act and sensitive information
- Transparency, necessity and consent
- Ownership, custodianship and bargaining
- Athlete-centric governance principles
The coordinator's description is honest about its shape: a lot of breadth, kept conceptual, with no deep dive into any single area. Teaching is blended. Weekly Canvas modules of videos, readings and interactive activities take the place of a lecture, and on-campus tutorials from Week 2 put the ideas to work in Microsoft Excel. The first five modules build a vocabulary for the rest of the degree.
Week 1 defines data through units, items and observations, climbs the DIKW pyramid from raw data to wisdom, maps where data sits across sport science, medicine, coaching, business and marketing, and introduces the data lifecycle from origin to reflection.
Week 2 sorts data into categorical and numerical types, sets out how to organise spreadsheets as tidy data, and asks whether data is meaningful by examining quality dimensions, survey question design and bias, along with top-down and bottom-up routes to analysis.
Week 3 summarises categorical data as proportions and continuous data by centre and spread, and compares pies, bars, histograms, boxplots, scatterplots and line graphs alongside principles of visual perception.
Week 4 introduces computational thinking and how to communicate with computers through programming languages and prompts, and Week 5 covers data governance, the Capability Maturity Model, Australian privacy law and athlete-centred ethics. Later weeks move to analytical intentions, relational data modelling, applied sport data, storytelling with data and big data.
Assessment combines a paired data management plan for an invented sports organisation, a working-with-data task and a final exam.
Worked example · free
Build a boxplot summary of a guard's points across eleven games
- 1Median: with 11 sorted values the middle one is the 6th, so the median is 10 points.
- 1Quartiles: the lower half 4, 6, 7, 8, 9 has median Q1 = 7; the upper half 12, 13, 15, 18, 34 has median Q3 = 15.
- 1Spread: IQR = Q3 minus Q1 = 15 minus 7 = 8 points.
- 1Fences: Q1 minus 1.5 times IQR = 7 minus 12 = negative 5; Q3 plus 1.5 times IQR = 15 plus 12 = 27. The 34-point game lies above 27, so it is an outlier and the upper whisker stops at 18.
- 1Typical game: the mean is 136 divided by 11, about 12.4, above the median of 10, so the data are right-skewed and the median with the IQR is the better summary.
Key terms
- Data
- Measurements or observations gathered so they can serve as a source of information, in many types and representations.
- Data unit
- The entity being studied, such as a player, a team or a club, about which data are collected.
- Observation
- One recorded value of a data item for one data unit, the content of a single cell in a tidy table.
- DIKW pyramid
- A model in which raw data is processed into information, connected into knowledge and distilled into wisdom, compressing at each level.
- Data lifecycle
- The sequence of stages a data source passes through, from origin and identification to dissemination and reflection.
- Ordinal data
- Categorical data whose categories follow a natural order, such as an education level or a Likert rating.
- Ratio scale
- A continuous scale with consistent intervals and an absolute zero, so values such as height can be compared as multiples.
- Tidy data
- A table layout with one column per variable, one row per observation and a separate table for each kind of observational unit.
- Data dictionary
- A separate file that explains every variable in a dataset, including its units and expected minimum and maximum.
- Interquartile range
- The spread of the middle half of the data, found as the upper quartile minus the lower quartile.
- Computational thinking
- A problem-solving framework of decomposition, abstraction, pattern recognition and algorithms.
- Data stewardship
- Assigning named roles for collecting, maintaining and reviewing data, so someone is accountable for each step.
- Capability Maturity Model
- A five-level scale, from Initial to Optimizing, used to rate how mature an organisation's data governance is.
92526 FAQ
How is the mark made up in this subject?
Three tasks make up the whole mark. Assessment 1, Data as an asset, is a paired data management plan worth 30%. Assessment 2, Working with data, is worth 40% and is due on 1 November. Assessment 3 is a final exam worth 30%, held in the examination period. The weights come from the assessment overview on Canvas.
What should I expect from the final exam?
The exam is worth 30% and is held in the examination period. The coordinator advises that material from both the Canvas content and the tutorials will be central to it, so revise the weekly modules and the Excel workbooks together. Check its length, format and permitted materials on Canvas and in your subject outline well before November.
What does the Data as an asset task involve?
Working in pairs, you design a data management plan for a sports organisation you invent. It introduces the organisation and its valuable data, rates its data governance with the Capability Maturity Model, and runs one data source through the data lifecycle, covering ownership and legislation. You submit a pre-recorded 10-minute presentation, an author statement and references, assessed against a 30-point rubric.
Which software do I need for the tutorials?
Microsoft Excel, Word and PowerPoint, installed on your own computer, which the subject resources page calls essential; UTS students can download Office free. The tutorials run on Excel from Week 2 and build from shortcuts and formatting to functions, absolute references, pivot tables and charts, so bring a laptop with Excel installed to every class.
Do I need to learn to code in R or Python?
No coding is marked. Week 4 explains why R and Python are popular for data science, being free, extensible and reproducible, and compares them with Excel, but the coordinator states that you will not be marked on programming in this unit. Learning one language is recommended for later study, and the computational thinking behind coding is part of the content.
What happens if I miss tutorials?
Sport and Exercise Science subjects expect attendance at every class. Report a missed class to your tutor within 2 working days, with documentation if it meets the Special Consideration criteria.
Where a third class is missed and the earlier absences met the criteria, a Student Engagement Activity, here a 10-minute audio-visual presentation, is due within 14 days; three or more non-compliant absences make you ineligible for the final assessment task.
Is this subject hard?
Its challenge is breadth rather than depth. Each week introduces a new framework, from the lifecycle and quality dimensions to Gestalt principles and governance maturity, while the tutorials add practical Excel skills.
The coordinator describes the content as conceptual rather than deep, so keeping pace with both the Canvas modules and the workbooks each week, and applying each framework to a sport you know, is the practical way to manage it.
Questions students ask in each 92526 chapter
- What is the difference between a data unit and a data item?
- A data unit is the entity you are studying, such as a player, team or business, and in a standard table it occupies a row. A data item is a characteristic measured or counted about that unit, such as sprint time or height, and it occupies a column. The value where a row meets a column is one observation. Chapter 01 · Defining Data and the DIKW Pyramid
- Where is data found in sport?
- Almost everywhere. The Week 1 slides name strength and conditioning, biomechanics, physiology, performance analysis, sports medicine, physiotherapy, psychology, nutrition, talent identification, coaching, business and marketing, and list the measurements each collects, from force plates and GPS to questionnaires, imaging, ticket sales and social media analytics. Chapter 02 · Where Data Comes From Across Sport
- What are the stages of the data lifecycle?
- The Week 1 interactive lists nine: origin, data identification, capture, transfer, storage, processing, analysis, dissemination and reflection. Each comes with a short set of questions, such as whether data is accessible at origin, whether a transfer channel is secure, or who should have access to the findings. Chapter 03 · The Data Lifecycle from Origin to Reflection
- What is the difference between nominal and ordinal data?
- Both are categorical. Nominal categories have no natural order, as with gender or nationality, while ordinal categories do, as with education level or a Likert rating. Ordinal data carries more information than nominal data, because the order itself is meaningful, but the gaps between ordinal categories are not guaranteed to be equal. Chapter 04 · Data Types and How Computers Store Them
- What does tidy data mean?
- Tidy data gives every variable its own column, every observation its own row and every kind of observational unit a table of its own. Any other arrangement is messy. A tidy table can be filtered, summarised, plotted and combined with other tables without first being rebuilt by hand. Chapter 05 · Organising Spreadsheets as Tidy Data
- What are the dimensions of data quality?
- The slides list seven, credited to IBM: completeness, the amount of usable data; uniqueness, the amount of duplication; validity, whether data matches the construct it measures; timeliness, whether it is ready in time; accuracy, correctness against an agreed source of truth; consistency, how stable a measure is across observations; and fitness for purpose, whether it meets a real need. Chapter 06 · Meaningful Data: Quality, Questions and Bias
- What is the difference between top-down and bottom-up analysis?
- Top-down analysis begins with a problem definition and a plan, scopes data that matches the problem, and fixes the design and models before collecting new data. Bottom-up analysis begins with data already held in databases or repositories, pools it across systems, and explores and models it to find insight. One starts from the question, the other from the data. Chapter 07 · Top-Down and Bottom-Up Analysis
- How is categorical data summarised?
- By counting how often each category occurs and converting those counts into proportions or relative percentages. The Week 3 slides define the population proportion as the number belonging to a category divided by the population size, and the sample proportion, written with a hat, as the same ratio within a sample, used to estimate the population value. Chapter 08 · Summarising and Graphing Categorical Data
- How do you choose the bins for a histogram?
- Calculate the range of the data, decide how many bins you want, and divide the range by the number of bins to get the bin width. Excel will pick a width automatically, but the Week 4 tutorial shows it can be too wide to reveal detail, so adjust the bin width or number of bins until the shape is informative. Chapter 09 · Histograms, Boxplots and Continuous Summaries
- How do you choose the right type of chart?
- The Week 3 page says the choice depends on the type of data and on how you want the reader to perceive it, meaning where, how and how quickly their attention should land. Charts are grouped by purpose: comparison, correlation, distribution, part-to-whole and data over time, with alternatives within each purpose. Chapter 10 · Choosing Graphs for Fast Perception
- What is the difference between a relative and an absolute reference?
- A relative reference such as C2 changes as a formula is copied, so filled down it becomes C3, C4 and so on. An absolute reference such as $B$2 is locked by dollar signs and stays on the same cell. Use absolute references for a single fixed value, such as a squad maximum or target, that every row divides by. Chapter 11 · Excel Functions, Referencing and Pivot Tables
- What are the four phases of computational thinking?
- Decomposition breaks a complex problem into smaller, manageable parts; abstraction focuses on the important information and ignores irrelevant detail; pattern recognition looks for similarities within and between problems; and algorithms develop a step-by-step solution or set of rules. A longer version adds generalisation, extending a solution to other problems. Chapter 12 · Computational Thinking and Talking to Computers
- What is data governance in sport?
- It is the overall management of an organisation's data so that it is available, usable, intact and secure. In sport that means policies, processes and standards for athletes' performance numbers, fan analytics and the club's financial records, plus clear roles, ownership and procedures for storing, sharing and using data, which supports better decisions and stakeholder trust. Chapter 13 · Data Governance Components and Maturity
- What does the Privacy Act require when a club collects athlete data?
- According to the module, the Privacy Act 1988 (Cth) treats most athlete data as personal information and much of it as sensitive health information. Clubs must be transparent through a privacy policy and collection notice, collect only what is reasonably necessary, obtain voluntary, informed and specific consent for sensitive data, limit use to the original purpose, and allow access and correction. Chapter 14 · Athlete Data: Privacy Law, Ownership and Ethics
How to study for the exam
Work through each week's Canvas module before its tutorial, because the tutorials assume the concepts and then put them into Excel.
Keep a one-page sheet of the named frameworks as you meet them, with their counts: the four DIKW levels, the nine lifecycle stages, the seven quality dimensions, the ten organising tips, the four wrangling verbs, the four phases of computational thinking, the seven governance components and the five maturity levels.
Then practise using each one on a sport you know, tying every term to a specific piece of evidence from that sport. Rebuild every tutorial workbook from a blank sheet at least once, including the BMI formula, an absolute reference, a pivot table and a boxplot. For Assessment 1, choose one data source early and walk it through every lifecycle stage.
In the final Stuvac, redo the worked examples by hand and revise the Week 6 to 12 modules directly from Canvas.
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