The book Discovering Knowledge in Data offers a practical yet rigorous introduction to the field of data mining, situated at the crossroads of predictive analytics, statistical analysis, and business intelligence. It is designed to guide readers whether students, analysts, or professionals through the full pipeline of turning raw data into actionable knowledge. Rather than treating data mining tools as “black boxes,” the authors adopt a “white box” methodology: they explain the mathematical and statistical underpinnings of each algorithm, illustrate how they work with real world datasets, and walk step by step through data preprocessing, exploratory analysis, modeling, and evaluation. The book covers a broad set of core topics and techniques: data cleaning and transformation; exploratory data analysis; supervised and unsupervised learning methods (classification, regression, clustering, association rules); neural networks; decision trees; k nearest neighbors; Kohonen maps; missing data imputation; model evaluation (including error metrics, overfitting, cross validation, cost/benefit analysis); and data summarization and visualization.