Activities - Frame the Problem

#	Name	Abbrev.	Guidance	Actions
Frame the Problem 6
1	Choose the analysis type	FPM-1	Match the driving business question to one of: descriptive, exploratory, inferential, predictive, causal, or mechanistic …
2	Formulate the driving questions	FPM-2	Write down the specific descriptive question(s) first ("what is happening / what is causing this"), …	Read the 'FPM-2 Formulate the driving questions' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. Write down the specific descriptive question(s) first ("what is happening / what is causing this"), then the predictive/prescriptive question(s) that follow from them ("what do we want to happen / what actions get us there"). Keep the questions in front of you for the rest of the playbook — every later chart and model exists to answer one of them.
3	Assess analyst readiness	FPM-3	Before opening any data, check you're bringing genuine interest/curiosity, skepticism, methodical practice, pattern-spotting, and enough …	Read the 'FPM-3 Assess analyst readiness' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. Before opening any data, check you're bringing genuine interest/curiosity, skepticism, methodical practice, pattern-spotting, and enough domain familiarity to know what "normal" looks like for this data. Also plan to alternate between the statistician, programmer, and domain-expert "hats" as the work proceeds.
4	Select candidate datasets from the datalake catalog	FPM-4	State what the "ideal" dataset for your questions would look like, then browse the provided …	Read the 'FPM-4 Select candidate datasets from the datalake catalog' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. State what the "ideal" dataset for your questions would look like, then browse the provided datalake catalog (a Phainestai-style dataset catalog) for the closest available match(es), accepting a smaller/less feature-rich substitute if the ideal isn't present. Record which catalog entries you selected and why. This step replaces raw web/DB/file acquisition with selection from a pre-built catalog, since this playbook assumes the datalake already exists.
5	Assess dataset quality against meaningfulness traits	FPM-5	For each selected catalog entry, check it against the traits of meaningful data: sufficient volume, …	Read the 'FPM-5 Assess dataset quality against meaningfulness traits' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. For each selected catalog entry, check it against the traits of meaningful data: sufficient volume, historical depth, consistency over time, multivariate coverage, atomic-level (not pre-aggregated) records, clean values, clear coding, dimensional structure, rich segmentation, and known pedigree/lineage. Drop or flag entries that fail badly on a trait that matters for your questions.
6	Write the Framing Brief	FPM-6	Consolidate the chosen analysis type, the driving questions, and the vetted dataset selection into a …	Read the 'FPM-6 Write the Framing Brief' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. Consolidate the chosen analysis type, the driving questions, and the vetted dataset selection into a single short brief. This is the contract the rest of the playbook works against — Phase 2 starts from it, and Phase 2's final conclusions get checked back against it.

Activities in Frame the Problem