Data Science Without Makeup: A Guidebook for End-Users, Analysts, and Managers

Description

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. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting. Cover Half Title Title Page Copyright Page Table of Contents foreword preface author I: the ugly truth 1 what is data science what data science is what data science is for why it is important to understand your data where data comes from glossary works cited 2 data science is hard iceberg of details domino of mistakes no second chance 3 our brain sucks correlation ≠ causation Reversing Cause and Effect Confounders Outliers data dredging (“p-hacking”) cognitive biases Confirmation Bias Optimism Bias Information Bias More Work Diluted Argument Lost Purpose glossary works cited II: a new hope 4 data science for people align data science efforts with business needs mind data science hierarchy of needs make it simple, reproducible, and shareable Simple Reproducible Shareable glossary works cited 5 quality assurance what makes QA difficult? Individual Mindset Team Culture Resources what is there to QA? Data Code Results how to QA all this? Communication Results Code glossary works cited 6 automation the automation story Phase 1: “Manual” Data Science Phase 2: Templates Phase 3: Script Phase 4: Full Automation underappreciated benefits Always Moving Forward Better Quality Assurance Fast Delivery questions to consider If When How glossary works cited III: people, people, people 7 hiring a data scientist pain vision transmission urgency system P. S. underappreciated qualities Written Communication Goal Orientation Conscientiousness Empathy P. P. S. overappreciated qualities Charisma Confidence glossary works cited 8 what a data scientist wants goal Purpose Challenge achievement Data Autonomy Focus Time Culture reward Impact Fair Recognition Growth data scientist types Idea: “Entrepreneur” Theory: “Academic” Tools: “Geek” Solution: “Doer” Recognition: “Careerist” glossary works cited 9 measuring performance time throughput goal achievement opinion works cited index