Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

by Foster Provost

In this book we introduce a collection of the most important fundamental concepts of data science. Some of these concepts are “headliners” for chapters, and others are introduced more naturally through the discussions (and thus they are not necessarily labeled as fundamental concepts). The concepts span the process from envisioning the problem, to applying data science techniques, to deploying the results to improve decision-making. The concepts also undergird a large array of business analytics methods and techniques. The concepts fit into three general types: Concepts about how data science fits in the organization and the competitive landscape, including ways to attract, structure, and nurture data science teams; ways for thinking about how data science leads to competitive advantage; and tactical concepts for doing well with data science projects. General ways of thinking data-analytically. These help in identifying appropriate data and consider appropriate methods. The concepts include the data mining process as well as the collection of different high-level data mining tasks. General concepts for actually extracting knowledge from data, which undergird the vast array of data science tasks and their algorithms.