PYTHON-08 · 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 | Automation, APIs and Databases with Python
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Sign in to save this learning pathPractical outcome: Design a dry-run file organiser operating only on a disposable folder.
Explain command-line arguments using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Explain configuration using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Explain file discovery using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Explain dry runs using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Use the first concepts from this section. Design a dry-run file organiser operating only on a disposable folder. 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 idempotent operations using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Explain structured logging using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Explain exit codes using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Explain recovery plans using a visual and one worked real-world example. Context: Design a dry-run file organiser operating only on a disposable folder. End with a prediction or check-for-understanding.
Design a dry-run file organiser operating only on a disposable folder. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Command-line arguments, Configuration, File discovery and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Specify a client for a fictional bounded API.
Explain http requests using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Explain json responses using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Explain status-code handling using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Explain timeouts using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Use the first concepts from this section. Specify a client for a fictional bounded API. 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 pagination using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Explain retry backoff using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Explain rate limits using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Explain credential boundaries using a visual and one worked real-world example. Context: Specify a client for a fictional bounded API. End with a prediction or check-for-understanding.
Specify a client for a fictional bounded API. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering HTTP requests, JSON responses, Status-code handling and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Build a local inventory design using a toy database.
Explain sqlite connections using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Explain parameterised sql using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Explain transactions using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Explain schema migrations using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Use the first concepts from this section. Build a local inventory design using a toy database. 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 row mapping using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Explain connection lifetimes using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Explain repository pattern using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Explain data export using a visual and one worked real-world example. Context: Build a local inventory design using a toy database. End with a prediction or check-for-understanding.
Build a local inventory design using a toy database. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering SQLite connections, Parameterised SQL, Transactions and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Build a local inventory design using a toy database. 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 Automation, APIs and Databases with 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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