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Data Science Without Makeup: A Guidebook for End-Users, Analysts, and Managers [Pehme köide]

  • Formaat: Paperback / softback, 178 pages, kõrgus x laius: 234x156 mm, kaal: 320 g, 6 Tables, black and white; 45 Line drawings, black and white; 45 Illustrations, black and white
  • Ilmumisaeg: 02-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367520680
  • ISBN-13: 9780367520687
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  • Formaat: Paperback / softback, 178 pages, kõrgus x laius: 234x156 mm, kaal: 320 g, 6 Tables, black and white; 45 Line drawings, black and white; 45 Illustrations, black and white
  • Ilmumisaeg: 02-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367520680
  • ISBN-13: 9780367520687

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.



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 professionals. 

Arvustused

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 Mikhails 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.

Mikhail's book is a clear sighted-look at why data science is hard, and why it is so rewarding. It tells the unvarnished truth about data science. The author's view from the trenches will resonate with data scientists, giving them vocabulary and frameworks to describe what they need from colleagues and clients. For sponsors and consumers of data science, this book will clarify what the data scientists are doing with their time. are doing with their time and why they need to do it. Chapter 2 (Data Science is Hard) is worth the price on its own -- and then Zhilkin gives us processes to help. An invaluable resource for, in plain language, framing data science as a difficult but valuable role in an organization, with strong advice on processes to maximize the effectiveness of that role. 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, Mikhails 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

Foreword ix
Preface xiii
Author xv
I The Ugly Truth
1 What is data science
5(18)
What data science is
5(5)
What data science is for
10(4)
Why it is important to understand your data
14(2)
Where data comes from
16(5)
glossary
21(1)
Works cited
22(1)
2 Data science is hard
23(14)
Iceberg of details
24(4)
Domino of mistakes
28(3)
No second chance
31(6)
3 Our brain sucks
37(26)
Correlation # causation
38(1)
Reversing Cause and Effect
39(1)
Confounders
39(2)
Outliers
41(4)
Data dredging ("p-hacking")
45(5)
Cognitive biases
50(1)
Confirmation Bias
51(2)
Optimism Bias
53(2)
Information Bias
55(1)
More Work
56(1)
Diluted Argument
56(3)
Lost Purpose
59(1)
glossary
60(1)
Works cited
60(3)
II A New Hope
4 Data science for people
63(16)
Align data science efforts with business needs
64(5)
Mind data science hierarchy of needs
69(3)
Make it simple, reproducible, and shareable
72(1)
Simple
72(2)
Reproducible
74(2)
Shareable
76(1)
glossary
77(1)
Works cited
77(2)
5 Quality Assurance
79(18)
What makes QA difficult?
80(1)
Individual Mindset
80(2)
Team Culture
82(1)
Resources
83(1)
What is there to QA?
84(1)
Data
85(1)
Code
86(2)
Results
88(1)
How to QA all this?
89(1)
Communication
89(3)
Results
92(3)
Code
95(1)
glossary
96(1)
Works cited
96(1)
6 automation
97(24)
The automation story
98(1)
Phase 1 "Manual" Data Science
98(1)
Phase 2 Templates
99(1)
Phase 3 Script
100(1)
Phase 4 Full Automation
101(1)
Underappreciated benefits
102(1)
Always Moving Forward
102(1)
Better Quality Assurance
103(1)
Fast Delivery
103(3)
Questions to consider
106(1)
If
106(3)
When
109(1)
How
110(7)
glossary
117(1)
Works Cited
117(4)
III people, people, people
7 Hiring a data scientist
121(24)
pain
123(2)
vision
125(2)
transmission
127(5)
urgency
132(3)
system
135(3)
P. S. underappreciated qualities
138(1)
Written Communication
139(1)
Goal Orientation
140(1)
Conscientiousness
141(1)
Empathy
141(1)
P. P. S. overappreciated qualities
142(1)
Charisma
142(1)
Confidence
143(1)
glossary
143(1)
Works cited
144(1)
8 What a data scientist wants
145(22)
goal
146(1)
Purpose
147(1)
Challenge
148(1)
achievement
149(1)
Data
149(1)
Autonomy
149(2)
Focus
151(3)
Time
154(1)
Culture
154(1)
reward
155(1)
Impact
155(2)
Fair Recognition
157(1)
Growth
158(2)
Data scientist types
160(2)
Idea: "Entrepreneur"
162(1)
Theory: "Academic"
162(1)
Tools: "Geek"
162(1)
Solution: "Doer"
163(1)
Recognition: "Careerist"
163(2)
glossary
165(1)
Works cited
165(2)
9 Measuring performance
167(10)
time
168(1)
throughput
169(2)
Goal Achievement
171(2)
opinion
173(3)
Works Cited
176(1)
Index 177
Mikhail Zhilkin is a Data Scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting.