Data science without makeup

Contributor(s):

Zhilkin, Mikhail [Autor]

Material type: Text

TextLanguage: English Publication details: CRC Press/Taylor & Francis Group, 2022Description: xv, 177 pages : illustrations ; 25 cmISBN:

9780367520687

Subject(s):

Contents:

Part One. The Ugly Truth 1. What is Data Science?-- 2. Data Science is Hard-- 3. Our Brain Sucks-- Part Two. A New Hope-- 4. Data Science for People-- 5. Quality Assurance-- 6. Automation-- Part Three. People, People, People-- 7. Hiring a Data Scientist-- 8 What a Data Scientist Wants-- 9. Measuring Performance--

Summary: 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 and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data from students to professional researchers and from early-career to seasoned professionals.

Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Book	TBS Barcelona Libre acceso	QA76.9.D32 ZHI (Browse shelf(Opens below))	Available		B04120

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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 and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data from students to professional researchers and from early-career to seasoned professionals.