The Data Science Handbook, 2nd Edition

1Introduction

The goal of this book is to turn you into a data scientist, and there are two parts to this mission. First, there is a set of specific concepts, tools, and techniques that you can go out and solve problems with today. They include buzzwords such as machine learning (ML), Spark, and natural language processing (NLP). They also include concepts that are distinctly less sexy but often more useful, like regular expressions, unit tests, and SQL queries. It would be impossible to give an exhaustive list in any single book, but I cast a wide net.

That brings me to the second part of my goal. Tools are constantly changing, and your long‐term future as a data scientist depends less on what you know today and more on what you are able to learn going forward. To that end, I want to help you understand the concepts behind the algorithms and the technological fundamentals that underlie the tools we use. For example, this is why I spend a fair amount of time on computer memory and optimization: they are often the underlying reason that one approach is better than another. If you understand the key concepts, you can make the right trade‐offs, and you will be able to see how new ideas are related to older ones.

As the field evolves, data science is becoming not just a discipline in its own right, but also a skillset that anybody can have. The software tools are getting better and easier to use, best practices are becoming widely known, and people are learning many of the key ...