Analysis design

As explained in Starting Data Analytics with Generative AI and Python

A planned specification of the steps, methods, data, possible results, and interpretations needed to answer an analytical question.

Analysis design is a planned protocol for answering a business question. Before making pivot tables or charts, specify the analyses, possible results, and what each result means for the decision. section 3.2

A useful design makes the analytical path explicit:

• State the business question and decision it must support. section 3.0
• List the planned transformations, analyses, and possible results. section 3.2
• Define the meaning and action associated with each result before inspecting the data. section 3.2
• Revise the protocol when preprocessing reveals unavailable analyses, new ideas, or changed stakeholder priorities. section 3.2

Turn results into decisions

For a pricing decision, define the rule before calculating: if the current price exceeds the purchase price, sell; if they are equal, hold; otherwise, buy. The rule prevents the analysis from becoming an after-the-fact theory fitted to convenient results. section 3.2

Generative AI can help turn a vague request into a detailed design, but treat its suggestions as a starting point. Describe the full problem, ask follow-up questions, inspect the proposed reasoning or code, and test the result against the intended question. section 1.2.1

Figure 1.1 Recommended generative AI–supported data analysis flow

Use the recommended flow to place planning, preparation, analysis, and iterative AI assistance within one end-to-end workflow. section 1.2.1

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes

Defines planned analyses, possible results, interpretations, and revisions, but does not prescribe a fixed protocol.

Examples

Uses a pricing decision rule and a generative-AI workflow for developing an analysis from a vague request.

What it assumes

Assumes a clear analytical goal and basic understanding of prepared data, while teaching how to plan results and interpretations.

What you can do after

Build an analysis protocol, revise it as evidence changes, and evaluate AI-generated suggestions against the business question.

How it connects

Connects analysis planning with experiment design, data preparation, descriptive analysis, statistical inference, and generative AI.

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Related concepts

• study designpart of · Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
• pipelineproduces · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
• evaluationimplemented by · Evaluation is the systematic assessment of a system, result, or approach against relevant criteria, evidence, or objectives.
• generative modelingused by · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.