PYTHON-03 · Secondary · 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 | Python Collections and Text
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Sign in to save this learning pathPractical outcome: Summarise a fictional week's study sessions.
Explain lists using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain indexing using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain slicing using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain list mutation using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain append and extend using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Use the first concepts from this section. Summarise a fictional week's study sessions. 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 removing items using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain tuples using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain unpacking using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain enumerate using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Explain zip using a visual and one worked real-world example. Context: Summarise a fictional week's study sessions. End with a prediction or check-for-understanding.
Summarise a fictional week's study sessions. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Lists, Indexing, Slicing and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Create an inventory model and detect duplicate item labels.
Explain dictionaries using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain key lookup using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain updating mappings using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain iterating mappings using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain nested structures using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Use the first concepts from this section. Create an inventory model and detect duplicate item labels. 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 sets using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain set union using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain set intersection using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain hashability using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Explain choosing a collection using a visual and one worked real-world example. Context: Create an inventory model and detect duplicate item labels. End with a prediction or check-for-understanding.
Create an inventory model and detect duplicate item labels. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Dictionaries, Key lookup, Updating mappings and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Clean a small hand-authored address-free text dataset.
Explain string methods using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain splitting text using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain joining text using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain whitespace using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain case normalisation using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Use the first concepts from this section. Clean a small hand-authored address-free text dataset. 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 unicode using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain string formatting using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain list comprehensions using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain dictionary comprehensions using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Explain copying versus aliasing using a visual and one worked real-world example. Context: Clean a small hand-authored address-free text dataset. End with a prediction or check-for-understanding.
Clean a small hand-authored address-free text dataset. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering String methods, Splitting text, Joining text 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 small hand-authored address-free text dataset. 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 Python Collections and Text, 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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