1: Introduction

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If this marks your first exposure to the new and exciting discipline of data science, you occupy an enviable position. Still in front of you is all the cool stuff, even the first few sparks of magic when you learn how to plug data into electrical sockets, perform automated prediction, and write the first gems of code to probe the depths of an interesting data set. I’m a bit jealous, tbh, but am also excited to explore it all again with you, which is the next best thing!

This field has changed the world like hardly any other has, and on an incredibly short time scale, too. Just a couple decades ago, businesses and organizations were routinely making major decisions based on gut feelings and anecdotal observations. Doctors eyeballed sets of symptoms and diagnosed patients largely based on what conditions they themselves had seen before, or seen recently. Online sellers gave product recommendations that made sense to them, completely missing patterns and trends that would become apparent if the characteristics and purchasing patterns of past customers were taken into account.

Part of the reason decision makers made these suboptimal choices was because it wasn’t yet clear how much punch data science would pack. Another reason was that the technology wasn’t there yet: the processing power and storage capacity to work with extremely large data sets wasn’t commonly available, and of course the data itself hadn’t all been gathered yet. No more! All these parts are here now. And somewhat incredibly, they’re all at your disposal for low (or even no) cost.

This is the era of data science. If you want to understand and make an impact on your world, I can honestly think of no better field to dive into than this one, no matter what your sphere of interest. The ability to command these techniques and tools gives you both great insight and great power to influence how life on planet Earth proceeds from this day forward.