Developing Analytic Talent

Learn what it takes to succeed in the the most in-demand tech job

Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code.

The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one.

• Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms

• Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists

• Features job interview questions, sample resumes, salary surveys, and examples of job ads

• Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations

Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Autorentext

Vincent Granville, Ph.D. is a data scientist with 15 years of big data, predictive modeling, and business analytics experience. He is the co-founder of Data Science Central, which includes a robust editorial platform, social interaction, forum-based technical support, the latest in technology tools and trends, and industry job opportunities.

Zusammenfassung
Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code.

The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one.

• Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms
• Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists
• Features job interview questions, sample resumes, salary surveys, and examples of job ads
• Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations
  Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Inhalt
Introduction xxi Chapter 1 What Is Data Science? 1

Real Versus Fake Data Science 2

Two Examples of Fake Data Science 5

The Face of the New University 6

The Data Scientist 9

Data Scientist Versus Data Engineer 9

Data Scientist Versus Statistician 11

Data Scientist Versus Business Analyst 12

Data Science Applications in 13 Real-World Scenarios 13

Scenario 1: DUI Arrests Decrease After

End of State Monopoly on Liquor Sales 14

Scenario 2: Data Science and Intuition 15

Scenario 3: Data Glitch Turns Data Into Gibberish 18

Scenario 4: Regression in Unusual Spaces 19

Scenario 5: Analytics Versus Seduction to Boost Sales 20

Scenario 6: About Hidden Data 22

Scenario 7: High Crime Rates Caused by Gasoline Lead. Really? 23

Scenario 8: Boeing Dreamliner Problems 23

Scenario 9: Seven Tricky Sentences for NLP 24

Scenario 10: Data Scientists Dictate What We Eat? 25

Scenario 11: Increasing Amazon.com Sales with Better Relevancy 27

Scenario 12: Detecting Fake Profiles or Likes on Facebook 29

Scenario 13: Analytics for Restaurants 30

Data Science History, Pioneers, and Modern Trends 30

Statistics Will Experience a Renaissance 31

History and Pioneers 32

Modern Trends 34

Recent Q&A Discussions 35

Summary 39

Chapter 2 Big Data Is Different 41

Two Big Data Issues 41

The Curse of Big Data 41

When Data Flows Too Fast 45

Examples of Big Data Techniques 51

Big Data Problem Epitomizing the

Challenges of Data Science 51

Clustering and Taxonomy Creation for Massive Data Sets 53

Excel with 100 Million Rows 57

What MapReduce Can't Do 60

The Problem 61

Three Solutions 61

Conclusion: When to Use MapReduce 63

Communication Issues 63

Data Science: The End of Statistics? 65

The Eight Worst Predictive Modeling Techniques 65

Marrying Computer Science, Statistics, and Domain Expertise 67

The Big Data Ecosystem 70

Summary 71

Chapter 3 Becoming a Data Scientist 73

Key Features of Data Scientists 73

Data Scientist Roles 73

Horizontal Versus Vertical Data Scientist 75

Types of Data Scientists 78

Fake Data Scientist 78

Self-Made Data Scientist 78

Amateur Data Scientist 79

Extreme Data Scientist 80

Data Scientist Demographics 82

Training for Data Science 82

University Programs 82

Corporate and Association Training Programs 86

Free Training Programs 87

Data Scientist Career Paths 89

The Independent Consultant 89

The Entrepreneur 95

Summary 107

Chapter 4 Data Science Craftsmanship, Part I 109

New Types of Metrics 110

Metrics to Optimize Digital Marketing Campaigns 111

Metrics for Fraud Detection 112

Choosing Proper Analytics Tools 113

Analytics Software 114

Visualization Tools 115

Real-Time Products 116

Programming Languages 117

Visualization 118

Producing Data Videos with R 118

More Sophisticated Videos 122

Statistical Modeling Without Models 122

What Is a Statistical Model Without Modeling? 123

How Does the Algorithm Work? 124

Source Code to Produce the Data Sets 125

Three Classes of Metrics: Centrality, Volatility, Bumpiness 125

Relationships Among Centrality, Volatility, and Bumpiness 125

Defining Bumpiness 126

Bumpiness Computation in Excel 127

Uses of Bumpiness Coefficients 128

Statistical Clustering for Big Data 129

Correlation and R-Squared for Big Data 130 A New Family of Rank Correlations 132&lt...