PYTHON-09 · 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 | Scientific Python and Data Analysis
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Sign in to save this learning pathPractical outcome: Compute a sensor-data summary with explicit shapes and units.
Explain array shapes using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Explain array dtypes using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Explain vectorised arithmetic using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Explain broadcasting using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compute a sensor-data summary with explicit shapes and units. 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 boolean masks using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Explain array indexing using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Explain aggregations using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Explain numerical precision using a visual and one worked real-world example. Context: Compute a sensor-data summary with explicit shapes and units. End with a prediction or check-for-understanding.
Compute a sensor-data summary with explicit shapes and units. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Array shapes, Array dtypes, Vectorised arithmetic and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Clean a synthetic dataset and document every transformation.
Explain dataframes using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain column types using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain missing values using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain filtering rows using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Use the first concepts from this section. Clean a synthetic dataset and document every transformation. 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 grouped summaries using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain joining tables using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain reshaping using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain date-time columns using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Explain validation checks using a visual and one worked real-world example. Context: Clean a synthetic dataset and document every transformation. End with a prediction or check-for-understanding.
Clean a synthetic dataset and document every transformation. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering DataFrames, Column types, Missing values and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Create a reproducible report that distinguishes patterns from claims.
Explain line plots using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain scatter plots using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain histograms using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain axes and units using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Use the first concepts from this section. Create a reproducible report that distinguishes patterns from claims. 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 error bars using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain misleading visual choices using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain reproducible notebooks using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain exported reports using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Explain statistical interpretation using a visual and one worked real-world example. Context: Create a reproducible report that distinguishes patterns from claims. End with a prediction or check-for-understanding.
Create a reproducible report that distinguishes patterns from claims. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Line plots, Scatter plots, Histograms and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Create a reproducible report that distinguishes patterns from claims. 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 Scientific Python and Data Analysis, 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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