LEARNING ACTIVITIES · HANDS-ON
Practice Labs
Six guided exercises, one for each chapter. Build workplace skills using consistent fictional retail data.
What to do
Read the matching textbook chapter, open the relevant dataset and complete the steps. Keep your output ready to use in the module assignment.
Map four analytics approaches to business decisions and present two use cases.
Chapter: Introduction to Data Analytics. Goal: Recognise the four types of analytics and explain how analytics roles differ.
- Open Retail_Sales.csv and locate the date, sales channel, order ID and net revenue columns.
- Write four business questions—one each for descriptive, diagnostic, predictive and prescriptive analytics.
- For each question, identify a required dataset, likely output and business decision.
- Present two workplace use cases, including a caution about what the available records cannot prove.
Deliverable: Four-row analytics map plus two business use cases and assumptions.
Write a problem statement, stakeholder register and measurable success criteria.
Chapter: Business Problem Solving. Goal: Turn a vague business concern into a measurable and actionable problem.
- Write a problem statement describing the August–September revenue decline without assuming the cause.
- Create a stakeholder register with sponsor, finance, e-commerce and data engineering roles.
- Document two stakeholder interview questions per role.
- Create one SMART objective, explicit scope, two assumptions, two constraints and three acceptance criteria.
Deliverable: Business problem statement, stakeholder register and success criteria.
Profile an unfamiliar dataset and construct a data-source inventory.
Chapter: Understanding Business Data. Goal: Choose appropriate data sources, types and levels of detail.
- Import Retail_Sales.csv and Retail_Customers.csv into Excel with appropriate field types.
- Identify the grain and key of each file; explain why customer ID may repeat in the order file.
- Profile completeness, duplicate IDs, date range, categorical values and numerical ranges.
- Complete a data-source inventory with owner, grain, refresh, sensitivity and limitations.
Deliverable: Data profile and data-source inventory with noted quality exceptions.
Build a KPI dictionary and compute baseline business metrics.
Chapter: KPIs and Business Metrics. Goal: Define business metrics precisely and calculate them with valid denominators.
- Load Retail_Sales.csv and Retail_Channel_Traffic.csv. Filter records by month and channel.
- Calculate monthly net revenue, order count, average order value and revenue growth.
- Calculate online conversion from online orders and eligible website sessions.
- Complete a KPI dictionary with formula, population, owner, source and interpretation.
Deliverable: KPI dictionary and validated monthly metrics worksheet.
Design a stepwise analytics workflow and validation checklist.
Chapter: Analytics Methodology. Goal: Plan an end-to-end, auditable analytical workflow.
- Write a project workflow covering business understanding, data sourcing, preparation, analysis and communication.
- Set source, integrity, reconciliation, interpretation and publication quality gates.
- Demonstrate a duplicate order ID check, missing-value count and monthly revenue reconciliation.
- Record how an unresolved data issue would be disclosed and escalated.
Deliverable: Stepwise analytical workflow and signed-off validation checklist.
Present a one-page insight brief containing findings, caveats and next actions.
Chapter: Communication Fundamentals. Goal: Communicate evidence-based findings with appropriate caveats and decisions.
- Create a one-page visual comparison of August and September revenue by channel.
- Write an observation, an insight and a recommendation as distinct statements.
- Describe at least two limitations or assumptions and avoid unsupported claims of causality.
- Deliver a one-page insight brief with actions, owners, timeframes and measurement plans.
Deliverable: One-page executive insight brief with an evidence-based action plan.