Joel Grus - Data Science from Scratch: First Principles with Python

by Joel Grus

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978-1-491-90142-7

[LSI]

Data scientist has been called the sexiest job of the 21st century, presumably by someone who has never visited a fire station. Nonetheless, data science is a hot and growing field, and it doesnt take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, well need billions and billions more data scientists than we currently have.

But what is data science? After all, we cant produce data scientists if we dont know what data science is. According to a Venn diagram that is somewhat famous in the industry, data science lies at the intersection of:

• Hacking skills
• Math and statistics knowledge
• Substantive expertise

Although I originally intended to write a book covering all three, I quickly realized that a thorough treatment of substantive expertise would require tens of thousands of pages. At that point, I decided to focus on the first two. My goal is to help you develop the hacking skills that youll need to get started doing data science. And my goal is to help you get comfortable with the mathematics and statistics that are at the core of data science.

This is a somewhat heavy aspiration for a book. The best way to learn hacking skills is by hacking on things. By reading this book, you will get a good understanding of the way I hack on things, which may not necessarily be the best way for you to hack on things. You will get a good understanding of some of the tools I use, which will not necessarily be the best tools for you to use. You will get a good understanding of the way I approach data problems, which may not necessarily be the best way for you to approach data problems. The intent (and the hope) is that my examples will inspire you try things your own way. All the code and data from the book is available onGitHub to get you started.

Similarly, the best way to learn mathematics is by doing mathematics. This is emphatically not a math book, and for the most part, we wont be doing mathematics. However, you cant really do data science without some understanding of probability and statistics and linear algebra. This means that, where appropriate, we will dive into mathematical equations, mathematical intuition, mathematical axioms, and cartoon versions of big mathematical ideas. I hope that you wont be afraid to dive in with me.

Throughout it all, I also hope to give you a sense that playing with data is fun, because, well, playing with data is fun! (Especially compared to some of the alternatives, like tax preparation or coal mining.)