BUSINESS-06 · Undergraduate · ENGLISH · PLANNED
Study an original business case, compare options, make a decision and defend implementation.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Business Administration · BBA/MBA | Business Mathematics, Statistics and Analytics
Playlist title reserved in the plan; no YouTube playlist or upload has been created yet.
Sign in to save this learning pathPractical outcome: Build an auditable spreadsheet for a fictional retail decision.
Explain percentages using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Explain ratios using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Explain weighted averages using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Explain index numbers using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Use the first concepts from this section. Build an auditable spreadsheet for a fictional retail decision. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain spreadsheet formulas using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Explain linear models using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Explain optimisation concepts using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Explain sensitivity analysis using a visual and one worked real-world example. Context: Build an auditable spreadsheet for a fictional retail decision. End with a prediction or check-for-understanding.
Build an auditable spreadsheet for a fictional retail decision. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Percentages, Ratios, Weighted averages and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Interpret a synthetic customer survey with appropriate uncertainty.
Explain descriptive statistics using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Explain sampling using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Explain probability using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Explain confidence intervals using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Use the first concepts from this section. Interpret a synthetic customer survey with appropriate uncertainty. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain hypothesis tests using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Explain association using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Explain regression using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Explain statistical pitfalls using a visual and one worked real-world example. Context: Interpret a synthetic customer survey with appropriate uncertainty. End with a prediction or check-for-understanding.
Interpret a synthetic customer survey with appropriate uncertainty. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Descriptive statistics, Sampling, Probability and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Create a dashboard that answers a specific business question.
Explain data cleaning using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Explain data visualisation using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Explain kpi design using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Explain forecasting using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Use the first concepts from this section. Create a dashboard that answers a specific business question. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain segmentation using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Explain experiment design using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Explain dashboard accessibility using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Explain decision memos using a visual and one worked real-world example. Context: Create a dashboard that answers a specific business question. End with a prediction or check-for-understanding.
Create a dashboard that answers a specific business question. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Data cleaning, Data visualisation, KPI design and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Create a dashboard that answers a specific business question. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Business Mathematics, Statistics and Analytics, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Case discussions, business plans, management simulations and original consulting projects. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
Open the interactive practice sampler →