PYTHON-11 · Advanced · ENGLISH · PLANNED
Predict the output, trace state, write a small program and debug it.
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 Python | Advanced Python and Performance
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Sign in to save this learning pathPractical outcome: Explain the behaviour of a small iterator-based pipeline.
Explain iterators using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain generators using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain yield from using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain closures using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Use the first concepts from this section. Explain the behaviour of a small iterator-based pipeline. 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 decorators using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain descriptors using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain attribute lookup using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain method resolution order using a visual and one worked real-world example. Context: Explain the behaviour of a small iterator-based pipeline. End with a prediction or check-for-understanding.
Explain the behaviour of a small iterator-based pipeline. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Iterators, Generators, yield from and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare execution models for a bounded toy workload.
Explain concurrency versus parallelism using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain threads using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain locks using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain processes using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain futures using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare execution models for a bounded toy workload. 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 async functions using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain await using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain task cancellation using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain backpressure using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Explain shared-state pitfalls using a visual and one worked real-world example. Context: Compare execution models for a bounded toy workload. End with a prediction or check-for-understanding.
Compare execution models for a bounded toy workload. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Concurrency versus parallelism, Threads, Locks and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Profile before optimising and document the measured tradeoff.
Explain benchmark design using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain profiling using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain allocation costs using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain caching using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain lazy evaluation using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Use the first concepts from this section. Profile before optimising and document the measured tradeoff. 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 memory management using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain reference counting using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain garbage collection using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain extension boundaries using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Explain interpreter-version differences using a visual and one worked real-world example. Context: Profile before optimising and document the measured tradeoff. End with a prediction or check-for-understanding.
Profile before optimising and document the measured tradeoff. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Benchmark design, Profiling, Allocation costs and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Profile before optimising and document the measured tradeoff. 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 Advanced Python and Performance, 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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