PYTHON-12 · Research · ENGLISH · PLANNED
Predict the output, trace state, write a small program and debug it.
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Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Python | Professional Python Projects and Research Tools
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Sign in to save this learning pathPractical outcome: Prepare a reusable original package with tests and a changelog.
Explain pyproject metadata using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Explain build artifacts using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Explain dependency groups using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Explain semantic versioning using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Use the first concepts from this section. Prepare a reusable original package with tests and a changelog. 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 release notes using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Explain license selection using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Explain documentation examples using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Explain compatibility testing using a visual and one worked real-world example. Context: Prepare a reusable original package with tests and a changelog. End with a prediction or check-for-understanding.
Prepare a reusable original package with tests and a changelog. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering pyproject metadata, Build artifacts, Dependency groups and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Deliver a tested offline analysis or automation tool using synthetic data.
Explain project scoping using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Explain architecture decisions using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Explain acceptance tests using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Explain cli ergonomics using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Use the first concepts from this section. Deliver a tested offline analysis or automation tool using synthetic data. 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 reproducibility manifests using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Explain data provenance using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Explain accessible reporting using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Explain failure recovery using a visual and one worked real-world example. Context: Deliver a tested offline analysis or automation tool using synthetic data. End with a prediction or check-for-understanding.
Deliver a tested offline analysis or automation tool using synthetic data. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Project scoping, Architecture decisions, Acceptance tests and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Reproduce a small algorithmic result and explain its limitations.
Explain reading library source using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Explain minimal reproductions using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Explain benchmark replication using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Explain numerical validation using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Use the first concepts from this section. Reproduce a small algorithmic result and explain its limitations. 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 code review practice using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Explain maintenance planning using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Explain technical communication using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Explain portfolio evidence using a visual and one worked real-world example. Context: Reproduce a small algorithmic result and explain its limitations. End with a prediction or check-for-understanding.
Reproduce a small algorithmic result and explain its limitations. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Reading library source, Minimal reproductions, Benchmark replication and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Reproduce a small algorithmic result and explain its limitations. 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 Professional Python Projects and Research Tools, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Coding, trace tables, tests and original portfolio 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.
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