Exploratory Data Analysis Starter Notebooks

Welcome — turn curiosity into experiments

This starter kit helps teams rapidly profile new datasets, surface patterns and anomalies, generate testable hypotheses, and prioritize concrete experiments. The notebooks are reproducible, pragmatic, and designed to minimize time spent on data wrangling so teams can focus on what to try next.

What’s included

• 00_environment_and_readme.ipynb — environment setup, dependencies, and an operations checklist to reproduce results (conda/env, pip, Dockerfile example).
• 01_data_profiling.ipynb — automatic and manual checks: column types, missing-value summaries, distribution plots, unique counts, cardinality, basic sanity checks, and a data-quality scorecard.
• 02_time_series_and_anomalies.ipynb — time-series decomposition, seasonal-trend-residual checks, rolling statistics, and starter anomaly detection with explainable visualizations.
• 03_correlation_and_segmentation.ipynb — correlation matrices, pairwise plots, simple feature-engineering probes, segmentation via clustering, and quick cohort comparisons.
• 04_hypothesis_capture_and_experiment_links.ipynb — structured hypothesis template, priority scoring (impact/effort/uncertainty), and links to runnable experiment skeletons or tickets.
• data_samples/ — small synthetic/sample datasets used by the notebooks so you can run them immediately.

Quickstart (10–30 minutes)

1. Clone the kit and create the environment: follow 00_environment_and_readme.ipynb. Use the provided environment.yml or Dockerfile for reproducible runs.
2. Open 01_data_profiling.ipynb and point it to your dataset (or the sample data) to get an immediate health and distribution snapshot.
3. Run 02_time_series_and_anomalies if your primary signals are temporal; run 03_correlation_and_segmentation for cross-sectional discovery.
4. Capture any promising signals in 04_hypothesis_capture_and_experiment_links.ipynb. Score each idea and pick 1–3 to convert into experiments.

Reproducibility and engineering notes

• Environment: include environment.yml and a lightweight Dockerfile. Prefer pinned versions for notebooks intended to be shared across teams.
• Data handling: include a clear README for data provenance, anonymization steps, and storage location (S3/Bucket, DB connection string patterns). Avoid embedding sensitive data in notebooks.
• Notebooks as modules: keep heavy transforms in small importable Python modules so notebooks remain readable and rerunnable.
• Versioning: recommend storing the kit and adapted copies in your organization’s repository so teams can fork and evolve without losing provenance.

From signals to experiments — practical next steps

Each notebook ends with explicit next-step suggestions that map findings to experiments. Examples:

• Data quality: missing-value spike in field X → experiment: instrument data collection fix for X and measure drop in missing rate over two weeks.
• Anomaly: sudden drop in conversion on a specific date → experiment: test rollback of recent deploy or simulate user flows to reproduce the failure in a staging environment.
• Segmentation: cluster shows a high-churn cohort → experiment: targeted intervention (email or pricing) for that cohort and measure retention lift.

Common pitfalls we help avoid (the Mal Hungers)

• Superficial checks that miss confounders — the toolkit emphasizes contextual checks (time windows, cohort controls, pivot tables).
• Analysis paralysis — each notebook includes a hypothesis capture section and a prioritization rubric so EDA ends in testable ideas, not endless charts.
• Reproducibility gaps — starter environment and lightweight engineering guidance reduce one-off notebooks that can’t be rerun.

How teams typically use this starter kit

1. Data scientist runs profiling to triage datasets and flags obvious quality risks.
2. Team lead or product manager reviews hypotheses captured in the hypothesis notebook and prioritizes experiments with the scoring rubric.
3. Engineers create lightweight experiment skeletons (feature flags, experiment config) and the team executes the highest-priority experiments within 1–2 sprints.

Customize and extend

The kit is intended as a reusable starting point. Useful adaptations include:

• Adding domain-specific checks (manufacturing sensor drift, clinical data validation rules, or financial compliance checks).
• Connecting outputs to issue trackers or experiment registries so hypotheses become tracked work items.
• Building simple dashboards that surface prioritized hypotheses and experiment status to stakeholders.

Ready-made improvements we recommend (capability ideas)

• Create an Interactive Experiment Intake form so anyone who runs a notebook can submit a structured hypothesis (fields: dataset, finding summary, proposed intervention, expected metric, priority). Saved submissions support traceability and experiment metrics.
• Package the kit as an Ownable Toolkit that teams can subscribe to and tailor (environment templates, custom checks, and site-specific guidance).
• Integrate lightweight automation to run profiling on schedule and surface alerts when quality scores degrade.

Concluding note

This Starter Kit is focused on making EDA actionable: quick profiling, trustworthy visualizations, reproducible code, and clear experiment handoffs. Use it to move from a question or suspicious signal to a prioritized experiment that delivers learning.