PYTHON-07 · Undergraduate · 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 | Algorithms and Data Structures in Python
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Sign in to save this learning pathPractical outcome: Compare two search implementations using counted operations.
Explain time complexity using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Explain space complexity using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Explain amortised cost using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Explain linear search using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare two search implementations using counted operations. 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 binary search using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Explain recursion using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Explain recursion base cases using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Explain divide and conquer using a visual and one worked real-world example. Context: Compare two search implementations using counted operations. End with a prediction or check-for-understanding.
Compare two search implementations using counted operations. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Time complexity, Space complexity, Amortised cost and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Choose structures for a toy task scheduler.
Explain stacks with lists using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Explain queues with deque using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Explain heaps using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Explain hash-table behaviour using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Use the first concepts from this section. Choose structures for a toy task scheduler. 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 linked nodes using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Explain binary search trees using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Explain adjacency lists using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Explain graph traversal using a visual and one worked real-world example. Context: Choose structures for a toy task scheduler. End with a prediction or check-for-understanding.
Choose structures for a toy task scheduler. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Stacks with lists, Queues with deque, Heaps and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Solve a bounded route or packing exercise with tests.
Explain breadth-first search using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain depth-first search using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain dijkstra's algorithm using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain greedy choices using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Use the first concepts from this section. Solve a bounded route or packing exercise with tests. 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 memoisation using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain dynamic programming using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain backtracking using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain sorting stability using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Explain property-based reasoning using a visual and one worked real-world example. Context: Solve a bounded route or packing exercise with tests. End with a prediction or check-for-understanding.
Solve a bounded route or packing exercise with tests. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Breadth-first search, Depth-first search, Dijkstra's algorithm and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Solve a bounded route or packing exercise with tests. 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 Algorithms and Data Structures in Python, 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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