Data Science is the foundation of all machine learning and the future of all complex decision-making processes, combining mathematical algorithms and machine learning techniques. Data Science provides the necessary structure for training Artificial Intelligence models. Statistical techniques greatly support data science algorithms. Throughout this book, many unsupervised learning techniques are developed from a methodological and practical perspective, with applications using Python software. Dimension Reduction Techniques: Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, and Multiple Correspondence Analysis are explored in depth, both from a theoretical and practical perspective