图书简介
"Having worked with Mikhail it does not surprise me that he has put together a comprehensive and insightful book on Data Science where down-to-earth pragmatism is the recurring theme. This is a must-read for everyone interested in industrial data science, in particular analysts and managers who want to learn from Mikhail‘s great experience and approach."
--Stefan Freyr Gudmundsson, Lead Data Scientist at H& ;M, former AI Research Lead at King and Director of Risk Analytics and Modeling at Islandsbanki.
"It tells the unvarnished truth about data science. Chapter 2 ("Data Science is Hard") is worth the price on its own—and then Zhilkin gives us processes to help. A must-read for any practitioner, manager, or executive sponsor of data science."
--Ted Lorenzen, Director of Marketing Analytics at Vein Clinics of America
"Mikhail is a pioneer in the applied data science space. His ability to provide innovative solutions to practical questions in a dynamic environment is simply superb. Importantly, Mikhail’s ability to remain calm and composed in high-pressure situations is surpassed only by his humility."
--Darren Burgess, High Performance Manager at Melbourne FC, former Head of Elite Performance at Arsenal FC
Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made.
Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts
I the ugly truth 1 what is data science 2 data science is hard 3 our brain sucks II a new hope 4 data science for people 5 quality assurance 6 automation III people, people, people 7 hiring a data scientist 8 what a data scientist wants 9 measuring performance
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