Introduction to Exploratory Analysis


Exploratory data analysis helps you understand a dataset before drawing conclusions or deciding what to do next. By reviewing its structure, quality, patterns, trends and unusual values, you can identify useful insights, raise further questions and choose appropriate methods for deeper analysis.

Technique Overview

Introduction to Exploratory Analysis

Introduction to Exploratory Analysis Definition

Exploratory data analysis (EDA) is the process of examining and summarising data to understand its structure, quality and main characteristics before drawing conclusions. It uses techniques such as summary statistics and visualisations to identify patterns, trends, relationships, unusual values and potential issues that may require further investigation (Tukey, 1977; Komorowski et al., 2016).

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Further Reading

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Introduction to Exploratory Analysis references (4 of up to 20) *

  • James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021) An Introduction to Statistical Learning: with Applications in R. 2nd edn. Springer.
  • Komorowski, M., Marshall, D.C., Salciccioli, J.D. and Crutain, Y. (2016). Exploratory Data Analysis. Secondary Analysis of Electronic Health Records, [online] pp.185–203. doi:10.1007/978-3-319-43742-2_15.
  • Oettl, F.C., Oeding, J.F., Feldt, R., Ley, C., Hirschmann, M.T. and Samuelsson, K. (2024). The artificial intelligence advantage: Supercharging exploratory data analysis. Knee Surgery Sports Traumatology Arthroscopy. doi:10.1002/ksa.12389.
  • Tufte, E.R. (1983). The Visual Display of Quantitative Information. Graphics Press.

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