Practical methods, starter projects, and adaptable templates for exploratory data analysis that surface patterns, hypotheses, and experiments.
A practical, step-by-step starter project that helps teams discover signals in data, evaluate readiness for experiments, generate testable hypotheses, and prioritize the next experiments. Includes concrete profiling checks, visualization suggestions, hypothesis templates, example scripting snippets, deliverables, and risk-mitigation guidance.
A practical, reproducible notebook template for quick exploratory data analysis (EDA): safe ingestion, basic profiling, seasonality and autocorrelation checks, segmentation and pivot exploration, key visualizations, lightweight anomaly checks, and a hypothesis-extraction checklist with next-experiment suggestions. Includes example code snippets and guidance for saving findings and instrumenting follow-up experiments.
A practical, reproducible EDA notebook scaffold with stepwise sections, actionable checks, ready-to-use code snippets (Python/pandas), an anomalies log template, suggested experiments, and a one-page summary template for non-technical stakeholders — designed to help teams turn EDA into testable experiments and clear decisions.
A practical, runnable notebook scaffold for fast EDA: data loading & profiling, missingness and anomaly checks, distributions and cohort plots, correlation matrices, simple feature transforms, quick segmentation checks, and a short hypothesis-capture template plus tips for documenting and sharing discoveries that lead to concrete experiments.