Task Plan: [Analytics Project Description]

<!-- WHAT: Roadmap for a data analytics or exploration session. WHY: Analytics workflows have different phases than software development — hypothesis testing, data quality checks, and statistical validation don't map to a generic build cycle. WHEN: Create this FIRST before starting any data exploration. Update after each phase. -->

Goal

<!-- WHAT: One clear sentence describing what you're trying to learn or produce. EXAMPLE: "Determine which user segments have the highest churn risk using last 90 days of activity data." -->

[One sentence describing the analytical objective]

Current Phase

<!-- WHAT: Which phase you're currently working on (e.g., "Phase 1", "Phase 3"). WHY: Quick reference for where you are. Update this as you progress. -->

Phase 1

Phases

Phase 1: Data Discovery

<!-- WHAT: Connect to data sources, understand schemas, assess data quality. WHY: Bad data produces bad analysis. This phase prevents wasted effort on unreliable inputs. -->

• Identify and connect to data sources
• Document schemas and field descriptions in findings.md
• Assess data quality (nulls, duplicates, outliers, date ranges)
• Estimate dataset size and query performance
• Status: in_progress

Phase 2: Exploratory Analysis

<!-- WHAT: Distributions, correlations, outliers, initial patterns. WHY: Understanding the shape of your data before testing hypotheses prevents false conclusions. -->

• Compute summary statistics for key variables
• Visualize distributions and relationships
• Identify outliers and anomalies
• Document initial patterns in findings.md
• Status: pending

Phase 3: Hypothesis Testing

<!-- WHAT: Formalize hypotheses, run statistical tests, validate findings. WHY: Moving from "it looks like X" to "we can confidently say X" requires structured testing. -->

• Formalize hypotheses from exploratory phase
• Select appropriate statistical tests
• Run tests and record results in findings.md
• Validate findings against holdout data or alternative methods
• Status: pending

Phase 4: Synthesis & Reporting

<!-- WHAT: Summarize findings, create visualizations, document conclusions. WHY: Analysis without clear communication is wasted work. This phase produces the deliverable. -->

• Summarize key findings with supporting evidence
• Create final visualizations
• Document conclusions and recommendations
• Note limitations and areas for further investigation
• Status: pending

Hypotheses

<!-- WHAT: Questions you're investigating, stated as testable hypotheses. WHY: Explicit hypotheses prevent fishing expeditions and keep analysis focused. EXAMPLE: 1. Users who logged in < 3 times in the last 30 days have > 50% churn rate (H1) 2. Feature X adoption correlates with retention (r > 0.3) (H2) -->

1. [Hypothesis to test]
2. [Hypothesis to test]

Decisions Made

<!-- WHAT: Analytical decisions with reasoning (e.g., choosing a test, filtering criteria). EXAMPLE: | Use median instead of mean | Revenue data is heavily right-skewed | | Filter to last 90 days | Earlier data uses a different tracking schema | -->

Decision	Rationale

Errors Encountered

<!-- WHAT: Every error you encounter, what attempt number it was, and how you resolved it. EXAMPLE: | Query timeout on raw table | 1 | Added date partition filter | | Null join keys in user_events | 2 | Inner join instead of left join, documented data loss | -->

Error	Attempt	Resolution
	1

Notes

• Update phase status as you progress: pending -> in_progress -> complete
• Re-read this plan before major analytical decisions
• Log ALL errors - they help avoid repetition
• Write query results and visual findings to findings.md immediately