Springer Texts in Business and Economics Data Science MBA

Many of these themes are illustrated using R examples, that familiarize the reader with data analysis, using these hands-on experiences to uniquely illustrate some important themes surrounding statistical analysis, and to let readers see for themselves how some popular statistical and data science techniques actually work. This text book focuses on what could be the most important challenge for firms to boost long-term productivity and competitiveness: digital strategy. It seeks to provide readers with a solid knowledge of the most relevant issues and concepts, that will be relevant to MBA students in real-world settings. The book discusses theoretical concepts relating to digital strategy, while also using hands-on data analysis in R software to illustrate some fundamental features and pitfalls of working with real-world data. The book starts by clarifying the meaning of relevant concepts (digitization vs digitalization; Machine learning, Artificial Intelligence), presents three leading models of digital transformation, and explains how digitalization has far-reaching implications for how organizations need to be structured. Then the book discusses the skills of a data scientist, and how digital transformation leads to new concerns surrounding ethics. Other themes include data quality, data pre-processing, data visualization, as well as the distinction between prediction and causal inference. Many of these themes are illustrated using R examples, that familiarize the reader with data analysis, using these hands-on experiences to uniquely illustrate some important themes surrounding statistical analysis, and to let readers see for themselves how some popular statistical and data science techniques actually work.