This is the peer-reviewed version of the article: Exploratory Data Analysis / Li Vigni, Mario; Durante, Caterina; Cocchi, Marina. - STAMPA. - 28: (2013), pp. 55-126. [10.1016/B978-0-444-59528-7.00003-X]. Elsevier Science Ltd. Terms of use: The terms and conditions for the reuse of this version of the manuscript are specified in the publishing policy. For all terms of use and more information, see the publisher's website. Chapter 3: Exploratory Data Analysis by Mario Li Vigni, Caterina Durante, and Marina Cocchi. The chapter outlines the concept of exploratory data analysis (EDA), descriptive statistics, projection techniques, clustering techniques, and remarks. It discusses the importance of EDA in food science, emphasizing the need for data-driven approaches to understand complex food systems. The chapter highlights the role of EDA in identifying patterns, relationships, and outliers in food-related data. It also introduces the use of graphical tools such as histograms, box and whisker plots, and scatter plots to summarize and visualize data. The chapter discusses the transition from EDA to exploratory multivariate data analysis (EMDA), which involves the use of projection techniques like principal component analysis (PCA) to reduce data complexity and extract meaningful information. The chapter emphasizes the importance of EDA in food science for understanding the relationships between food composition, processing conditions, and end properties such as healthiness and consumer perception. It also discusses the challenges of analyzing high-dimensional data in food science and the need for multivariate analysis techniques to handle such complexity. The chapter concludes with a discussion on the practical applications of EDA and EMDA in food science, highlighting their importance in data interpretation and decision-making.This is the peer-reviewed version of the article: Exploratory Data Analysis / Li Vigni, Mario; Durante, Caterina; Cocchi, Marina. - STAMPA. - 28: (2013), pp. 55-126. [10.1016/B978-0-444-59528-7.00003-X]. Elsevier Science Ltd. Terms of use: The terms and conditions for the reuse of this version of the manuscript are specified in the publishing policy. For all terms of use and more information, see the publisher's website. Chapter 3: Exploratory Data Analysis by Mario Li Vigni, Caterina Durante, and Marina Cocchi. The chapter outlines the concept of exploratory data analysis (EDA), descriptive statistics, projection techniques, clustering techniques, and remarks. It discusses the importance of EDA in food science, emphasizing the need for data-driven approaches to understand complex food systems. The chapter highlights the role of EDA in identifying patterns, relationships, and outliers in food-related data. It also introduces the use of graphical tools such as histograms, box and whisker plots, and scatter plots to summarize and visualize data. The chapter discusses the transition from EDA to exploratory multivariate data analysis (EMDA), which involves the use of projection techniques like principal component analysis (PCA) to reduce data complexity and extract meaningful information. The chapter emphasizes the importance of EDA in food science for understanding the relationships between food composition, processing conditions, and end properties such as healthiness and consumer perception. It also discusses the challenges of analyzing high-dimensional data in food science and the need for multivariate analysis techniques to handle such complexity. The chapter concludes with a discussion on the practical applications of EDA and EMDA in food science, highlighting their importance in data interpretation and decision-making.