Exploratory Data Analysis Starter Notebook

Overview

This starter notebook is designed to move teams quickly from a raw dataset to prioritized, testable hypotheses. It emphasizes pragmatic reproducibility, fast checks for common pitfalls, clear visuals you can iterate on, and a hypothesis log that turns exploration into experiments.

Quick orientation

Open the notebook and run the cells in order on a representative sample (or the whole dataset when feasible). Each section is short and focused so you can iterate fast. Use the hypothesis log to capture findings and decide concrete next steps.

Notebook structure and purpose

1. Environment & metadata — packages, dataset name, author, date, random seed, and brief description of source/context.
2. Data ingestion — load data from CSV/Parquet/SQL with a single canonical loader cell. Keep a small sample view and a reproducible ingest pipeline.
3. Sanity checks & missingness — row/column counts, duplicate keys, missing value summary, and quick checks for unexpected datatypes.
4. Distribution & univariate plots — histograms, boxplots, and categorical frequency bars with recommended binning and log-scale options.
5. Pairwise relationships & correlation scan — correlation matrix, scatterplot matrix for prioritized columns, and targeted scatter + loess for signal checking.
6. Segmentation prompts — group-by summaries, pivot-tables, and simple cohort comparisons to surface heterogeneity.
7. Anomaly & outlier checks — z-score or IQR-based flags, time-series anomaly checks when applicable.
8. Hypothesis log — structured table to record: hypothesis, how to test it, target metric, required data, confidence, and next action.
9. Prioritization & next steps — suggested rubric to pick 1–3 experiments: expected impact, ease of testing, and data quality risk.
10. Reproducibility notes — sample-size caveats, versioning guidance, and how to save notebook outputs and artifacts.

What to run first (practical sequence)

1. Run the Environment & metadata cell so package versions and seed are recorded.
2. Load a small representative sample (1–5%) and run Sanity checks to make sure the loader behaves as expected.
3. Run Missingness and Distribution on the sample. If results look stable, run on the full dataset or larger sample.
4. Scan correlations and segmentation prompts to form 3–5 candidate hypotheses, then log them in the Hypothesis table.
5. Apply the prioritization rubric and select the top hypothesis to validate with a small focused test or experiment.

Hypothesis log (recommended schema)

A simple, reproducible log helps prevent wandering exploration. Use this table for each candidate finding:

• Hypothesis: short, testable statement (If X then Y).
• Test: what analysis or experiment will confirm/refute it (A/B test, time-window comparison, regression adjustment).
• Metric: primary metric to measure (conversion rate, mean spend, error rate).
• Data needed: fields, time window, sample size estimate.
• Confidence: quick qualitative (Low / Medium / High) and the reason.
• Next action: implement experiment, collect more data, perform causal adjustment, or deprioritize.

Example hypothesis entry

Hypothesis: Users from Channel A have 20% lower retention than Channel B after 30 days.
Test: Compare 30-day retention with propensity-score stratification or matched cohorts.
Metric: 30-day retention rate (binary)
Data needed: user_id, signup_date, channel, retention_flag_30d
Confidence: Medium (signal in raw cohort, unadjusted)
Next action: Run stratified comparison and check confounders (signup source, geolocation)

Sample-size caveats and quick rules

• If a subgroup has fewer than ~50–100 events for the metric of interest, treat results as exploratory and consider collecting more data.
• Beware multiple comparisons: record how many hypotheses you're testing and use conservative thresholds or false-discovery adjustments for automated scans.
• For time-based metrics, check for seasonality and upstream changes (deployments, holidays) before drawing conclusions.

Practical checks to avoid common EDA traps

• Data quality: verify unique keys, consistent timestamps, and stable encoding/locale issues.
• Leaky features: ensure features weren’t created using future or outcome data.
• Spurious correlations: prioritize domain-plausible explanations and validate with segmentation and temporal logic.
• Overfitting in visual exploration: prefer simple aggregations and hold out a small temporal or random test set for confirmation.

Adaptations for different dataset sizes

• Small datasets — avoid over-binning and show raw points; bootstrap uncertainty estimates where possible.
• Large datasets — use sampling with stratification for quick iteration; compute aggregates with efficient libraries or SQL for full-run checks.
• Streaming / event data — compute rolling aggregates and look for distributional shifts over time rather than single-snapshot histograms.

Reproducibility & sharing

• Record package versions and seed values in the Environment cell.
• Save key artifacts (aggregates, flagged rows, plots) to a data/artifacts folder and reference their path in the hypothesis log.
• Export the hypothesis log as CSV or connect to the platform’s submission API to capture findings centrally (see Capability notes below).

Minimal example snippets (Python/pandas style)

# Ingestion
import pandas as pd
df = pd.read_parquet('data/transactions.parquet')

# Missingness summary
missing = df.isna().sum().sort_values(ascending=False)

# Distribution example
df['amount'].hist(bins=50)

# Correlation scan
corr = df.select_dtypes('number').corr().abs().unstack().sort_values(ascending=False)

How teams typically use this Starter Notebook

• Data scientist: quick profiling to form causal questions and experiments.
• Product manager: identify potential feature opportunities or risks with concrete metrics to test.
• Analyst/engineer: validate data quality and export cleaned artifacts for pipelines.

Next steps and prioritization rubric

For each logged hypothesis, score:

• Impact (1–5): potential business or scientific value
• Effort (1–5): engineering and data cost
• Data risk (1–5): confidence in data quality)

Prioritize high impact, low effort, and low data risk items for immediate tests. For promising but high-risk items, schedule a data-quality remediation step first.

Capability and adaptation opportunities (platform-aware)

This static notebook is a useful starting point. It becomes significantly more powerful when paired with interactive platform capabilities:

• Capture hypothesis log entries and save them to the platform via a simple interactive form so findings become searchable organizational memory.
• Provide variant notebook templates (small-sample, full-run, streaming) that teams can copy and tailor per their domain.
• Bundle the notebook as part of a reusable EDA Toolkit that sites or teams can own, version, and extend.

Where this resource sits in the Discovery & Innovation Hub

Use this starter notebook as the exploration phase of an innovation pipeline: discover signals, capture hypotheses, prioritize experiments, and then hand off to experiment/runbook resources that implement tests and measure outcomes.

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