The recent advances in the field of Data Science, particularly in Machine Learning, are becoming increasingly feasible and practically relevant for new software solutions. These solutions have the ability to learn from data and make intelligent predictions. However, for these systems to be successful, they must be constructed considering the best practices of the Software Engineering field to fully meet the needs of clients. This book aims to consolidate the field of Software Engineering for Data Science and train professionals interested in working on Data Science in the construction of systems based on Machine Learning. It demonstrates how to build these end-to-end systems, adapting and applying best practices within that specific context.