This chapter from the book "Exploratory Data Analysis" by Mario Li Vigni, Caterina Durante, and Marina Cocchi discusses the importance of exploratory data analysis (EDA) in food science and related fields. The authors emphasize that EDA is crucial for understanding complex systems in food production and consumption, which are influenced by environmental, socio-economic, and regulatory factors. EDA shifts the focus from hypothesis-driven to data-driven approaches, allowing researchers to uncover hidden patterns and relationships in data without preconceived notions. The chapter outlines the key concepts of EDA, including descriptive statistics, projection techniques, and clustering methods. It highlights the importance of visual tools such as frequency histograms, box plots, and scatter plots for understanding data distributions and relationships. The authors also introduce principal component analysis (PCA) and other projection techniques, explaining how these methods can reduce data complexity and facilitate the identification of underlying structures. PCA is described in detail, covering its definition, derivation, and application in food data analysis. The chapter explains how PCA decomposes data into principal components, which capture the variance in the data, and how these components can be visualized using scatter plots and biplots. The authors provide guidelines for selecting the appropriate number of principal components and interpreting the results. Overall, the chapter emphasizes the value of EDA in food science for generating hypotheses, understanding data patterns, and improving the quality and safety of food products.This chapter from the book "Exploratory Data Analysis" by Mario Li Vigni, Caterina Durante, and Marina Cocchi discusses the importance of exploratory data analysis (EDA) in food science and related fields. The authors emphasize that EDA is crucial for understanding complex systems in food production and consumption, which are influenced by environmental, socio-economic, and regulatory factors. EDA shifts the focus from hypothesis-driven to data-driven approaches, allowing researchers to uncover hidden patterns and relationships in data without preconceived notions. The chapter outlines the key concepts of EDA, including descriptive statistics, projection techniques, and clustering methods. It highlights the importance of visual tools such as frequency histograms, box plots, and scatter plots for understanding data distributions and relationships. The authors also introduce principal component analysis (PCA) and other projection techniques, explaining how these methods can reduce data complexity and facilitate the identification of underlying structures. PCA is described in detail, covering its definition, derivation, and application in food data analysis. The chapter explains how PCA decomposes data into principal components, which capture the variance in the data, and how these components can be visualized using scatter plots and biplots. The authors provide guidelines for selecting the appropriate number of principal components and interpreting the results. Overall, the chapter emphasizes the value of EDA in food science for generating hypotheses, understanding data patterns, and improving the quality and safety of food products.