Preface

Suppose you have a magical black box. You put a huge amount of data into it, from everywhere you can find. And say that each of these inputs are in different formats and each format, in turn, represents several distinct time periods, frequencies, and seasons, as well as different characteristics. Imagine that you can then push a button and voilà! Out pops new knowledge from that hodgepodge of input. You now know the likelihood that the next event will occur, you can predict behavior; you have quantified risk, the propensity to act, and well-defined emerging patterns. And all this magically produced output would be ready to use—in the format needed to take action. This is amazing, isn’t? You just fill some box with lots of different stuff and get results that are easy to understand and that can immediately be put into use.

That black box is analytics. And it isn’t a brand-new discipline but is heavily used in most industries these days. Analytics combines mathematics, statistics, probability, and heuristics theories. So now, after this brief description, we all understand that analytics is easy, right?

Mathematical disciplines, including statistics, probability, and others, have always been assigned to practical applications in some form or another, including those of business. Sometimes the relationship between the method and the application is quite clear, and sometimes it is not. As human beings, we have used trade since we started to relate to each other. Because of ...

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