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Create intermediate Works with: Any AI

VS Code Notebook and Data Exploration Workspace Set-Up

Structured brief

Organise a data project with notebooks and scripts.

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Plan a VS Code workspace for data analysis. Languages and libraries: [LANGUAGES_AND_LIBRARIES]. Data sources and size: [DATA_SOURCES]. Who will reuse the work: [AUDIENCE]. Problems with notebooks I have had: [PROBLEMS].

Recommend a project folder tree (data, notebooks, src, outputs, docs), environment management, selecting the interpreter or kernel, numbered notebook names such as 01_load, 02_clean, 03_explore with parameters in the first cell, rules for moving repeated code into scripts, and the .gitignore entries (data files, outputs, environment folders, checkpoint folders). Test reproducibility with the clean run: restart the kernel and run all cells, and say what to do when it fails; warn about hidden state. Explain notebook, variable viewer and data viewer features [VERIFY]. README skeleton: Purpose, Data, How to run, Results, Limitations; example for an invented sales analysis (source, extraction date, where stored, no personal data in the repository, environment versions, notebooks in order, known limits).

Pre-commit and sharing checklist: outputs cleared or reviewed, no secrets or personal data, no absolute paths, notebook runs top to bottom, environment exported [VERIFY tool names], a purpose statement added. Keep personal data out of the repository, use anonymised samples for sharing, and store raw data in the approved location. Suggest how to compare notebook changes in review [VERIFY] and a tidy-up routine at the end of each project. Results should be reproducible by someone else from the README alone.

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