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Roman's Data Science: How to monetize your data Paperback – 9 September 2021
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An introduction to the field of data analysis written in jargon-free language that is not bogged down by programming code and mathematical formulas. It covers the most essential topics in the fields of data science, machine learning and business intelligence that you are likely to come across on a regular basis.
The main goal is to help readers get the most out of their data, make business decisions and create information products – all without paying over the odds.
In a career spanning over 20 years, the author has worked as a junior data analyst, headed up the analytics division of an $10-billion company and co-founded a Recommender Systems startup. The text was edited by a professional journalist. It contains QR codes and links with links you can follow if you want a deeper understanding of the topics covered.
From the Publisher
- In Russia, the book was sold out in three weeks.
- The book is based on the author’s personal experience – it is not a boring textbook!
- It provides a complete Data Science picture without coding and math.
Those who are new to data science will learn exactly what the business needs in this area are, as well as different approaches to data analysis and an effective way to master machine learning.
Startups will learn how to get their data science divisions up and running quickly. They will also be introduced to three approaches to analyzing A/B tests.
Managers will learn about task management, money efficiency and the hypothesis pipeline in data science.
- ASIN : B09FRZX625
- Publisher : Independently published (9 September 2021)
- Language : English
- Paperback : 304 pages
- ISBN-13 : 979-8465129695
- Dimensions : 15.24 x 1.75 x 22.86 cm
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Reviewed in Germany on 25 October 2021
The book really covers almost all aspects of analytics department in a company: what are the tasks, what tools are used, who and how works with it all, what to expect and how to grow.
Some things I would have filed a little differently, some I did not agree with, but in the whole book I did not come across some detached from reality things. It feels like the author has picked up more than one rake along the way from starting his career in analytics to writing this book.
The only thing I'd call a drawback of the book is the math chapter.
The whole book to that point assumes that the reader has no knowledge of statistics. And there's the "normal distribution", the "histogram", "the median is the value that divides the sample in half" and so on. Readers who're not in math won't understand it at all, I've checked. And then we also talk about the distance of percentiles from the median, without explaining what percentiles are. I would move the paragraph with the definition to the place before the first use of the term. The exponential distribution is also without any prior definition. In general, the chapter goes sharply into mathematics.
I would definitely recommend this book to executives who have not yet had experience with analytics beyond basic tools like spreadsheets in excel.
I would recommend this book to analysts who have had professional experience but don't yet really understand the role of analytics in business.
And I would recommend this book to people who already have some experience and understanding of how business works, but are just about to get into analytics.
- The chapters of the book can be read independently of each other
- The material is served in small pieces - easy to read
Without knowledge of the most basic concepts and definitions, it will be a little difficult