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Brad Pitt might not be the first person you think of when talking about data science. However, he both produced and stared in the hit movie Moneyball (Miller, 2011). This told the tale of a minor baseball team and how it used data science to compete with the major teams with significantly larger budgets. It’s a great underdog film, but what is really interesting is how the data science was adopted and changed baseball, in fact changed all sports. If movies aren’t your thing, the film is based on a book, which tells the true story (Lewis, 2004). You can see the impact of data science not only in sport but also in the world around you. Want to stream some TV or music? You are bound to have recommendations made to you, but have you ever thought that the TV programmes themselves are only made because the ‘data’ says they will be popular? All courtesy of data science. Of course they are the visible manifestation of its use. It has applications across society. Look closely at any sector and you’ll find data science at work. Data science has, and continues to have, a huge impact on the society we live in. It is hard to separate debate around artificial intelligence (AI) from the movie Terminator (Cameron, 1984), where the robots take over and wage war on humankind. Of more immediate concern might be the effect of AI on the nature of work. We are not here to talk about AI in great detail, but we are keen to explain the foundations to the data science that allows AI to have the capability that it does. Although largely outside the scope of this book, AI might be seen by some as the current pinnacle of data science. We cover their relationship in Chapter 14. We have written this book as an introduction to data science – to help you unlock its potential and be part of this force that is fundamental to shaping the society in which we live. Before we explore this, let’s first consider what we mean by data science. There is debate as to where the term data science originates, and no consensus on its exact meaning. However, the definitions tend to focus on two aspects: the disciplines involved in undertaking the analysis; and the type of analysis undertaken. Advancements in digital technology have been a key driver behind its development, enabling the more effective use of mathematical and statistical methods in understanding and interacting with the world around us. However, for this to have value, it requires domain knowledge to identify the problems and opportunities to which it is applied. As you will discover, data science is a multidisciplinary activity that will require you to develop skills and knowledge in all the above.