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Developing Analytic Talent


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Table of Contents

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 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 Asymptotic Distribution and Normalization 134 Computational Complexity 137 Computing q(n) 137 A Theoretical Solution 140 Structured Coefficient 140 Identifying the Number of Clusters 141 Methodology 142 Example 143 Internet Topology Mapping 143 Securing Communications: Data Encoding 147 Summary 149 Chapter 5 Data Science Craftsmanship, Part II 151 Data Dictionary 152 What Is a Data Dictionary? 152 Building a Data Dictionary 152 Hidden Decision Trees 153 Implementation 155 Example: Scoring Internet Traffic 156 Conclusion 158 Model-Free Confidence Intervals 158 Methodology 158 The Analyticbridge First Theorem 159 Application 160 Source Code 160 Random Numbers 161 Four Ways to Solve a Problem 163 Intuitive Approach for Business Analysts with Great Intuitive Abilities 164 Monte Carlo Simulations Approach for Software Engineers 165 Statistical Modeling Approach for Statisticians 165 Big Data Approach for Computer Scientists 165 Causation Versus Correlation 165 How Do You Detect Causes? 166 Life Cycle of Data Science Projects 168 Predictive Modeling Mistakes 171 Logistic-Related Regressions 172 Interactions Between Variables 172 First Order Approximation 172 Second Order Approximation 174 Regression with Excel 175 Experimental Design 176 Interesting Metrics 176 Segmenting the Patient Population 176 Customized Treatments 177 Analytics as a Service and APIs 178 How It Works 179 Example of Implementation 179 Source Code for Keyword Correlation API 180 Miscellaneous Topics 183 Preserving Scores When Data Sets Change 183 Optimizing Web Crawlers 184 Hash Joins 186 Simple Source Code to Simulate Clusters 186 New Synthetic Variance for Hadoop and Big Data 187 Introduction to Hadoop/MapReduce 187 Synthetic Metrics 188 Hadoop, Numerical, and Statistical Stability 189 The Abstract Concept of Variance 189 A New Big Data Theorem 191 Transformation-Invariant Metrics 192 Implementation: Communications Versus Computational Costs 193 Final Comments 193 Summary 193 Chapter 6 Data Science Application Case Studies 195 Stock Market 195 Pattern to Boost Return by 500 Percent 195 Optimizing Statistical Trading Strategies 197 Stock Trading API: Statistical Model 200 Stock Trading API: Implementation 202 Stock Market Simulations 203 Some Mathematics 205 New Trends 208 Encryption 209 Data Science Application: Steganography 209 Solid E?Mail Encryption 212 Captcha Hack 214 Fraud Detection 216 Click Fraud 216 Continuous Click Scores Versus Binary Fraud/Non-Fraud 218 Mathematical Model and Benchmarking 219 Bias Due to Bogus Conversions 220 A Few Misconceptions 221 Statistical Challenges 221 Click Scoring to Optimize Keyword Bids 222 Automated, Fast Feature Selection with Combinatorial Optimization 224 Predictive Power of a Feature: Cross-Validation 225 Association Rules to Detect Collusion and Botnets 228 Extreme Value Theory for Pattern Detection 229 Digital Analytics 230 Online Advertising: Formula for Reach and Frequency 231 E?Mail Marketing: Boosting Performance by 300 Percent 231 Optimize Keyword Advertising Campaigns in 7 Days 232 Automated News Feed Optimization 234 Competitive Intelligence with 234 Measuring Return on Twitter Hashtags 237 Improving Google Search with Three Fixes 240 Improving Relevancy Algorithms 242 Ad Rotation Problem 244 Miscellaneous 245 Better Sales Forecasts with Simpler Models 245 Better Detection of Healthcare Fraud 247 Attribution Modeling 248 Forecasting Meteorite Hits 248 Data Collection at Trailhead Parking Lots 252 Other Applications of Data Science 253 Summary 253 Chapter 7 Launching Your New Data Science Career 255 Job Interview Questions 255 Questions About Your Experience 255 Technical Questions 257 General Questions 258 Questions About Data Science Projects 260 Testing Your Own Visual and Analytic Thinking 263 Detecting Patterns with the Naked Eye 263 Identifying Aberrations 266 Misleading Time Series and Random Walks 266 From Statistician to Data Scientist 268 Data Scientists Are Also Statistical Practitioners 268 Who Should Teach Statistics to Data Scientists? 269 Hiring Issues 269 Data Scientists Work Closely with Data Architects 270 Who Should Be Involved in Strategic Thinking? 270 Two Types of Statisticians 271 Using Big Data Versus Sampling 272 Taxonomy of a Data Scientist 273 Data Science?s Most Popular Skill Mixes 273 Top Data Scientists on LinkedIn 276 400 Data Scientist Job Titles 279 Salary Surveys 281 Salary Breakdown by Skill and Location 281 Create Your Own Salary Survey 285 Summary 285 Chapter 8 Data Science Resources 287 Professional Resources 287 Data Sets 288 Books 288 Conferences and Organizations 290 Websites 291 Definitions 292 Career-Building Resources 295 Companies Employing Data Scientists 296 Sample Data Science Job Ads 297 Sample Resumes 297 Summary 298 Index 299

About the Author

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.


"I strongly recommend this book for readers whose background is related to data science, statistics, information technology and management, computer science, business analytics, and so on." (Online Information Review, May 2015)

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