Five non-technical books every data scientist should read


In 2010, I wrote my first line of R code in a class at the University of Washington. When I realized that code was more powerful than spreadsheets, I was immediately hooked. Over the past decade, I have seen the term "data science" used extensively and the rise and fall of buzzwords like big data, business intelligence, data analytics, and now artificial intelligence. The course that started this new phase of my life at the University of Washington was "Financial Econometrics," and like the deep learning course today, the large classroom was full. At that time, the financial crisis was still fresh in everyone's mind.

The Wealth Formula: this is the story of the birth of MIT in the early days of the Kelly Criterion. The Kelly standard is said to come from great financial success. You'll learn about the origins of the famous card-making prank in Claude Shannon, the father of information theory, and later in EdThorpe's BeatingtheMakers. Thorpe is now considered to be the godfather of quantitative hedge funds. Most importantly, this book shows how a good model can never be ignored, but a bad one can ruin you.

Chaos : unlocking the new science : a book containing a detailed history of the latest science. There is both a history of chaos theory and a commentary on the subject. This book will give the reader an understanding of the limitations of our ability to simulate the real world. Due to the nature of nonlinear processes, many of the deep learning models being developed and used are not yet truly understood. This book will help you understand these limitations. In addition, a comprehensive assessment of the life and work of Benoit Mandelbrot makes this book a must-read for any data scientist.

The Theory That Wouldn't Die: This book focuses on Bayesian formulas and the history of Bayesian statistics and its rival, frequency statistics. Statistical history and commentary on key technical topics in plain language make this book essential. You will learn about some of the greatest thinkers in history, such as PierreLaplace and R.A. Fischer, and how their philosophies have shaped the world's approach to data over the centuries. These five books, while not exhaustive, will help establish a philosophical foundation for data scientists dealing with real-world problems.


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