When I started working on the book "Roman's Data Science: How to Monetize Your Data," I wanted to bridge the gap between the interests of business and Data Science. We data scientists are under pressure of high expectations. Many executives expect that implementing ML models will solve almost all of their business problems. We, in turn, are also victims of our expectations. Many are surprised to realize that it only takes 10% of their time to create helpful ML models when entering the profession. Fixing problems and proving that these models benefit the company in production take another 90% of the time. So in this book, I decided to show how things are. It will be helpful for managers who assign tasks to data scientists. And for the data scientists who perform these tasks. I hope that through this book, we will be able to get more value out of the data.

I address the book for a wide range of readers. The use of mathematics is kept to a minimum (it is almost absent, except for two chapters), and the programming code is wholly omitted. All this opens up the possibility for a wide range of readers who have no idea about mathematics and programming to become closely acquainted with data analytics and understand what we do. When I wrote the book, I wrote it as if I were writing it for myself at the beginning of my career, mentioning mistakes and failed experiments. If there had been a time machine, I would certainly have sent it to myself. Like the anti-hero Biff did in "Back to the Future Part 2", sending Grays Sports Almanac to himself in the past to make money on the sweepstakes. Would I have made fewer mistakes then? I think just as many, only in a different way :), more interesting.