BSAN2201 Principles of Business Analytics
The Complete Study & Assessment Guide · S2 2026BSAN2201 Overview
- Semester 2, 2026
- Level 2
- Coursework assessment
- Business analytics
Principles of Business Analytics introduces business analytics as the connection between business data, analytical methods and decisions.
- Assessment focus Evidence-led coursework across five connected tasks
- Quantitative core Statistics, correlation and simple linear regression
- Case-study move Test capability against a stated comparator
- How to prepare Classify, calculate, interpret and recommend
What you get in the full BSAN2201 Study & Assessment Guide
- 8 chapters, 38 pages — every assessed topic, week by week
- 32 original practice questions, each with a model answer
- 8 worked examples, solved the way BSAN2201 marks
- 13 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Case Study Report (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How BSAN2201 is assessed
| Component | Weight | Format |
|---|---|---|
| Structured Summary of an Article | 10% | In-class activity in Tutorial 3, submitted at the end of the tutorial |
| Article Review | 20% | Written review, due 18 September 2026 at 3:00 pm |
| Case Study Report | 40% | PowerPoint, 20 slides or fewer, due 30 October 2026 at 3:00 pm |
| Poster Presentation | 20% | Single A3 page, approximately five minutes, presented in the Teaching Week 13 tutorial |
| Reflection (Video) | 10% | MP4, four to five minutes, due 13 November 2026 at 3:00 pm |
Assessment structure
What other students are asking about BSAN2201
- Where can I find lecture notes or study guides for BSAN2201 at UQ?
- The BSAN2201 study guide in AskSia Library covers Principles of Business Analytics in 8 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 BSAN2201?
- No final exam is listed in the offering this AskSia Library guide is built from; BSAN2201 is assessed by Structured Summary of an Article 10%, Article Review 20%, Case Study Report 40%, Poster Presentation 20%, Reflection (Video) 10%.
- What does BSAN2201 cover on data types and management?
- In AskSia Library's BSAN2201 guide, the Data Types and Management chapter explains measurement scales, structure, data quality, privacy and the management pipeline.
- What does BSAN2201 cover on descriptive analysis and visual choice?
- In AskSia Library's BSAN2201 guide, the Descriptive Analysis and Visual Choice chapter explains central tendency, dispersion, chart selection and honest interpretation.
- What does BSAN2201 cover on prediction and optimisation methods?
- In AskSia Library's BSAN2201 guide, the Prediction and Optimisation Methods chapter explains regression, classification, machine learning, simulation and constrained decisions.
Current BSAN2201 dates
| Date | Item | Control |
|---|---|---|
| 18 September 2026, 3:00 pm | Article Review | Published Semester 2, 2026 due date |
| 30 October 2026, 3:00 pm | Case Study Report | Published Semester 2, 2026 due date |
| 13 November 2026, 3:00 pm | Reflection (Video) | Published Semester 2, 2026 due date |
Dates are as published in the Semester 2, 2026 assessment briefing notes and rubrics. Confirm exact deadlines and submission settings in the live LMS.
What BSAN2201 covers
Eight teaching chapters connect data, methods, applications and assessment-ready business reasoning.
Business Analytics Decisions
Business questions, decision owners, and descriptive, predictive and prescriptive purposesBusiness analytics is the connection between business data, analysis and a decision.
- Decision owners and business questions
- Business intelligence and business analytics
- Descriptive analytical purpose
- Predictive analytical purpose
- Prescriptive analytical purpose
- Evidence-to-action boundaries
Data Types and Management
Measurement scales, structure, data quality, privacy and the management pipelineData classification determines which summaries and models are defensible. Numeric data divides into discrete counts and continuous measurements, while non-numeric data divides into nominal labels and ordinal rankings.
- Numeric and non-numeric data
- Discrete and continuous variables
- Nominal and ordinal categories
- Structured and unstructured records
- Big-data management stages
- Quality, privacy and security
Descriptive Analysis and Visual Choice
Central tendency, dispersion, chart selection and honest interpretationDescriptive analytics compresses current or historical observations into patterns a decision-maker can understand. Measures of central tendency describe the centre, while range and standard deviation describe spread.
- Mean, median and mode
- Range and standard deviation
- Outliers and sensitivity
- Category comparisons
- Time and relationship charts
- Axes, denominators and units
Prediction and Optimisation Methods
Regression, classification, machine learning, simulation and constrained decisionsPredictive methods estimate a number or category, while prescriptive methods select an action. Regression suits numeric targets, classification suits categorical targets, and time-series methods preserve temporal order.
- Regression and numeric targets
- Classification and categories
- Time-series prediction
- Machine-learning evaluation
- Optimisation objectives and constraints
- Simulation under uncertainty
Correlation and Linear Regression
Association, fitted lines, coefficient meaning, substitution and extrapolationCorrelation summarises the strength and direction of a linear relationship, with a coefficient from minus one to plus one. Its sign gives direction and its absolute value describes strength, but neither establishes causation.
- Correlation direction and strength
- Scatterplots and outliers
- Predictor and target variables
- Intercept and slope meaning
- Substitution with units
- Causation and extrapolation limits
Building an Analytics Advantage
Resources, knowledge, routines, comparators and sustainable capabilityThe resource-based view explains performance differences through the resources and capabilities firms possess and combine. The knowledge-based view treats knowledge as a particularly strategic resource.
- Resource-based view
- Knowledge-based view
- Data, people and technology
- Decision routines and governance
- Relative competitive outcomes
- Imitation and sustainable capability
Internal Analytics Applications
Financial, operational and people decisions with cross-functional guardrailsInternal applications improve processes inside the organisation. Financial analytics supports reporting, enterprise performance management, cost analysis and risk decisions.
- Financial reporting analytics
- Performance and cost management
- Operational flow and capacity
- Maintenance and quality evidence
- People and talent analytics
- Target and guardrail metrics
External Analytics Applications
Marketing, supply chains, customer value and the case-study argumentExternal applications manage customer demand and supplier relationships. Marketing analytics tracks and analyses marketing activity to improve performance, customer experience and returns.
- Customer attraction and retention
- Pricing and offer response
- Marketing return evidence
- Supplier and inventory decisions
- Fulfilment and customer value
- Case-study capability testing
The Semester 2, 2026 teaching sequence begins with the field and its decision purpose, then moves through data types and big-data management, descriptive methods, predictive and prescriptive techniques, statistical reasoning, analytics-based competitive advantage, and internal and external applications. The quantitative core includes measures of centre and dispersion, correlation and simple linear regression.
The strategic core asks how data, people, technology and routines combine into an analytical capability that creates relative business value.
The course assessment follows the same development path. A structured summary trains accurate compression and evaluation of one article. The article review asks for synthesis across five sources and a managerially relevant judgement.
The case-study report then tests a focal business against an explicit standard or comparator, interprets the findings with confidence and limitations, and develops recommendations plus transferable lessons.
A single-page poster compresses that case without losing its argument, while the reflection video traces a specific change in the student's perspective and future action.
This guide keeps method and decision together. Every chapter begins with the business question, identifies the compatible data and analytical type, works an original example, and ends with a boundary on what the evidence can claim.
The goal is not to attach sophisticated vocabulary to a business story. It is to make the chain visible: define the decision, classify the evidence, choose the method, check the arithmetic, interpret uncertainty and recommend an action that follows from the result.
Worked example · free
Use a regression result without overclaiming
- +1Substitute Training = 8: Revenue = 42 + 3.5(8).
- +1Multiply: 3.5 times 8 = 28.
- +1Add: predicted revenue = 70 thousand dollars.
- +1Interpret the slope as a predicted 3.5-thousand-dollar revenue increase per additional thousand dollars of training, without claiming causation.
Key terms
- Business analytics
- The disciplined use of business data and analytical methods to support a decision or strategy.
- Descriptive analytics
- Analysis of current or historical data to identify what happened and reveal patterns.
- Predictive analytics
- Analysis that estimates a future value, event or category from patterns in data.
- Prescriptive analytics
- Analysis that selects or recommends an action using objectives, predictions and constraints.
- Ordinal data
- Categorical data with a meaningful order but no guaranteed equal distance between adjacent categories.
- Standard deviation
- A measure describing how observations are dispersed around their mean.
- Correlation coefficient
- A value from minus one to plus one that summarises the strength and direction of a linear relationship.
- Linear regression
- A method that fits a linear relationship between a target variable and one or more predictors.
- Optimisation
- A mathematical method for finding the best feasible choice under an objective and constraints.
- Analytical capability
- The combined data, people, technology, governance and routines that repeatedly turn analysis into decisions.
- Marketing analytics
- The tracking and analysis of marketing evidence to improve performance, customer experience and returns.
- Supply-chain analytics
- The use of evidence to improve sourcing, inventory, logistics, fulfilment and supplier decisions.
BSAN2201 FAQ
How is the course assessed?
The published Semester 2 structure contains five coursework components: a structured summary worth 10%, an article review worth 20%, a case-study report worth 40%, a poster presentation worth 20%, and a reflection video worth 10%. The numeric weights total 100%, and each task builds a different stage of the same evidence-to-decision process.
What quantitative skills should I practise?
Practise choosing and interpreting measures of centre and spread, reading correlation by direction and strength, and using a simple linear regression equation. Show every substitution with units, check whether the sign matches the plotted relationship, and state the boundary between association and causation before making a business recommendation.
How do descriptive, predictive and prescriptive analytics differ?
Descriptive analytics identifies what happened, predictive analytics estimates what is likely to happen, and prescriptive analytics recommends what to do next. A real problem can use all three in sequence. The safest classification comes from the required output: a pattern, a forecast or a selected action under constraints.
What makes a strong case-study report?
A strong report frames an exact guiding question, supports the business context, tests analytical capability against an explicit standard or comparator, interprets evidence with confidence and limitations, and makes recommendations that answer the question. It also distinguishes lessons that transfer to other firms from features unique to the focal business.
How should I use visuals in the poster?
Use a visual when it carries analytical meaning: a comparison, process, relationship, journey or evidence pattern. The poster rubric rewards a layout that guides the reader through the argument and charts or diagrams that do analytical work. Decorative graphics consume space without helping the reader understand the capability judgement or conclusion.
What is the best way to study each week?
After the lecture, rebuild the main concept as a decision question, complete one original classification or calculation, and write a three-sentence interpretation containing the result, limitation and action. Keep an evidence ledger for readings with columns for claim, support, limitation and managerial implication, because that record feeds the later review and case study.
Questions students ask in each BSAN2201 chapter
- How do I identify the analytical type in a scenario?
- Look at the required output. A summary or pattern is descriptive, an estimate of a future outcome is predictive, and a selected action is prescriptive. If the scenario contains several stages, label each stage and explain which output directly answers the manager's decision. Chapter 01 · Business Analytics Decisions
- Why can a number still be nominal data?
- Numbers can serve as identifiers or category codes without representing magnitude. Customer number 1047 is not larger in a meaningful business sense than customer number 1046. Arithmetic on those codes would add meaning the variable does not possess. Chapter 02 · Data Types and Management
- When should I prefer the median to the mean?
- Prefer the median when the distribution is strongly skewed or a few extreme observations would make the mean unrepresentative of the middle experience. Keep the mean when total burden or resource use matters, and report both when they reveal different operational facts. Chapter 03 · Descriptive Analysis and Visual Choice
- How do I choose between regression and classification?
- Start with the target. Use regression when the desired output is numeric, such as demand or cost, and classification when the output is a category, such as churn or no churn. Similar predictors can feed both, but the outputs and evaluation measures differ. Chapter 04 · Prediction and Optimisation Methods
- What does a negative correlation mean?
- It means the variables tend to move in opposite directions: higher values of one accompany lower values of the other. The sign says nothing about causal direction, and the absolute value should be interpreted with the scatterplot because outliers or curves can mislead. Chapter 05 · Correlation and Linear Regression
- Why is buying software not enough to create capability?
- Software is only one resource. Capability requires reliable data, people who understand the business and method, governance, timely decision rights and a feedback routine. If output arrives after the choice or nobody owns exceptions, the technical asset does not become business value. Chapter 06 · Building an Analytics Advantage
- How do internal application areas overlap?
- A single process decision can change financial cost, operational flow and employee workload. Analyse those effects together. A cheaper staffing plan may increase queues, errors and turnover, so a cross-functional result is more credible than improvement on one local dashboard measure. Chapter 07 · Internal Analytics Applications
- How do marketing and supply-chain analytics connect?
- Marketing creates and measures demand, while the supply chain must fulfil the promise. A promotion can lift response but damage customer value if stock accuracy, substitutions or delivery capacity fail. Evaluate the customer outcome and economics across both systems. Chapter 08 · External Analytics Applications
How to prepare for the assessments
Study in four passes. First, classify the business question as descriptive, predictive or prescriptive and identify the decision owner. Second, classify the available data by measurement scale and structure so that you do not apply arithmetic to labels or ignore a processing requirement.
Third, work the quantitative method by hand: calculate centre and spread, read the direction and strength of correlation, or substitute into the regression equation with units. Fourth, write the managerial interpretation, including the decision, uncertainty and a guardrail metric. For assessment preparation, maintain a source matrix rather than isolated notes.
Record each article's claim, evidence, setting, limitation and practical implication. Organise the article review around themes, reuse the matrix to define a case-study comparator, and convert the report's central relationship into the poster visual. For the reflection, keep short notes on moments when evidence changed your initial view; a specific turning point is easier to explain than a general statement about learning.
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