The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues. The course covers terminology and concepts, tools and methods, use cases and success stories of data science applications. The course explains what is Data Science and why it is so hyped. It discusses the value that Data Science can create, the main classes of problems that Data Science can solve, the difference is between descriptive, predictive and prescriptive analytics, and the roles of machine learning and artificial intelligence. From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories and realistic examples.