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method#Data#Analytics#Product

Exploratory Data Analysis (EDA)

EDA is a structured visual and statistical approach for initial investigation of datasets to identify patterns, outliers and assumptions.

Exploratory Data Analysis (EDA) is an iterative, methodical approach to examining datasets using visualization, summary statistics and simple transformations.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

Databases (PostgreSQL, BigQuery)Notebook environments (Jupyter, VS Code)Profiling and visualization tools (ydata-profiling, seaborn)

Principles & goals

Proceed iteratively: quickly form and validate hypotheses.Combine visual and numeric: use charts plus summary statistics.Include domain knowledge: interpret together with subject matter experts.
Discovery
Domain, Team

Use cases & scenarios

Compromises

  • Overgeneralizing from random patterns.
  • Incorrect imputation decisions can introduce bias.
  • Incomplete documentation leads to lack of reproducibility.
  • Involve domain experts early.
  • Reproducibility: version notebooks and reports.
  • Iterative refinement: work from coarse to detailed.

I/O & resources

  • Raw data as CSV, Parquet or database export
  • Schema documentation and field descriptions
  • Access to visualization and analysis tools
  • EDA report with visualizations and statistics
  • Recommended cleaning and imputation rules
  • Prioritized hypotheses for follow-up analyses

Description

Exploratory Data Analysis (EDA) is an iterative, methodical approach to examining datasets using visualization, summary statistics and simple transformations. The goal is to uncover patterns, outliers and hypotheses for further analysis. EDA reduces uncertainty and informs model selection, feature engineering and business questions.

✔

  • Rapid identification of data issues and outliers.
  • Improved feature selection and model robustness.
  • Better alignment between data and business requirements.

✖

  • Not automated: requires human interpretation.
  • Scalability with very large raw data can be limited.
  • Subjectivity: different analysts may reach different conclusions.

  • Missing value rate

    Percentage of missing entries per field as an indicator of data quality.

  • Number of detected outliers

    Number of unusual values according to defined method per dataset.

  • Correlation between key variables

    Measure to identify redundant or highly linked features.

EDA in customer churn analysis

Examining customer behavior, segments and churn patterns to identify relevant predictors.

EDA for payment fraud detection

Detecting unusual transaction patterns and anomalies as a basis for feature development.

Product metrics exploration

Analysis of usage metrics to prioritize improvements and identify measurement errors.

1

Define data selection and sampling

2

Generate exploratory visualizations and statistics

3

Document issues and anomalies

4

Derive imputation and cleaning rules

5

Produce report and recommendations for stakeholders

⚠️ Technical debt & bottlenecks

  • Insufficiently documented transformation rules.
  • Missing standard pipelines for reproducibility.
  • Legacy data formats hinder automated analyses.
Missing metadataCompute and storage limits on raw dataUnclear responsibilities for data quality
  • Drawing conclusions from small, non-representative samples only.
  • Automatically removing outliers without cause analysis.
  • Interpreting EDA results as definitive proof of causality.
  • Mistaking correlation for causation.
  • Ignoring time zone or timestamp inconsistencies.
  • Over-reliance on automated profiling tools.
Fundamentals of statistics and probabilityData manipulation with Python / pandas or equivalentVisualization skills and interpretation of plots
Availability of representative samplesData transparency and metadata qualityTooling support for visualization and profiling
  • • Privacy and compliance restrictions
  • • Limited availability of data samples
  • • Lack of standardized metrics