ECONOMICS-04 · Undergraduate · ENGLISH · PLANNED
State the question and assumptions, draw the model, analyse evidence and test competing explanations.
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 Economics | Mathematics, Statistics and Data for Economics
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Sign in to save this learning pathPractical outcome: Solve a small consumer optimisation problem and explain assumptions.
Explain functions using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Explain partial derivatives using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Explain elasticities as derivatives using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Explain constrained optimisation using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Use the first concepts from this section. Solve a small consumer optimisation problem and explain assumptions. 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 lagrange multipliers using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Explain comparative statics using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Explain matrix notation using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Explain difference equations using a visual and one worked real-world example. Context: Solve a small consumer optimisation problem and explain assumptions. End with a prediction or check-for-understanding.
Solve a small consumer optimisation problem and explain assumptions. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Functions, Partial derivatives, Elasticities as derivatives and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Describe a synthetic household survey with uncertainty estimates.
Explain random variables using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Explain distributions using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Explain expectations using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Explain variance using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Use the first concepts from this section. Describe a synthetic household survey with uncertainty estimates. 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 sampling using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Explain confidence intervals using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Explain hypothesis tests using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Explain statistical power using a visual and one worked real-world example. Context: Describe a synthetic household survey with uncertainty estimates. End with a prediction or check-for-understanding.
Describe a synthetic household survey with uncertainty estimates. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Random variables, Distributions, Expectations and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Clean a fictional price dataset and document a reproducible workflow.
Explain units and metadata using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Explain missing data using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Explain outliers using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Explain deflating prices using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Use the first concepts from this section. Clean a fictional price dataset and document a reproducible workflow. 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 index construction using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Explain weighted averages using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Explain reproducible tables using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Explain ethical data use using a visual and one worked real-world example. Context: Clean a fictional price dataset and document a reproducible workflow. End with a prediction or check-for-understanding.
Clean a fictional price dataset and document a reproducible workflow. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Units and metadata, Missing data, Outliers and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Clean a fictional price dataset and document a reproducible workflow. 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 Mathematics, Statistics and Data for Economics, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Economic diagrams, synthetic datasets, calculations and evidence-based policy memos. 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.
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