Data Science is an interdisciplinary field that uses methods, algorithms, processes and systems to extract knowledge and conclusions from structured and unstructured data. It combines elements of statistics, computer science, mathematics and analytical techniques to solve problems, make predictions and generate value from data. It relies on big data to discover patterns, trends and relationships that can be used for decision making in various industries. Is and important support for Artificial Intelligence. Data Science uses two types of techniques: supervised learning, which trains a model with known input and output data to predict future results, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of these supervised learning techniques are developed throughout this book from a methodological point of view and from a practical point of view with applications through Python software. The following techniques are covered: kNN (Nearest Neighbor), SVM (Support Vector Machine), Naïve Bayes, Ensemble Methods (Bagging, Boosting, Stacking, Voting and Blending), and Neural Network Models (Multilayer Perceptron, Radial Basis Network, Hopfield Network, LSTM Networks, RNN Netwoks and GRU networks).