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Full Description
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.
Contents
Contents.- Preface.- Chapter 1 Introduction and definitions.- Chapter 2 Digital Transformation of Organizations.- Chapter 3 Big Data technologies and Architecture.- Chapter 4 the Data Science process.- Chapter 5 Ethics of Data Science and AI.- Chapter 6 Working with Data.- Chapter 7 The User Experience UEX.- Chapter 8 Data Visualization.- Chapter 9 Descriptions statistical associations.- Chapter 10 Prediction.- Chapter 11 Text as data.- Chapter 12 Causal inference.- Chapter 13 Conclusion.- References.
