This book is also part of our apprenticeship. Part of the content as well as new content is in a separate document called Addendum. Click here to download the addendum. The book is available on Barnes and Noble. Also, read our article on strong correlations to see how various sections of our book apply to modern data science. If you start from zero, read my data science cheat sheet first: it will greatly facilitate the reading of my book.

My second book - Data Science 2.0 - can be checked out here. The book described on this page is my first book.

**About the Author**

Dr. Vincent Granville is a well rounded, visionary data science executive with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security), domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT) and roles (data scientist, founder, CFO, CEO, HR, product development, marketing, media buyer, operations, management consulting).

Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, automated exploratory data analysis with data dictionaries, data videos as a visualization tool, automated data science, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.

Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals (including Journal of Number Theory, IEEE Pattern analysis and Machine Intelligence, Journal of the Royal Statistical Society, Series B), a Wiley book on data science, and is an invited speaker at international conferences. He also holds a few patents on scoring technology, and raised $6 MM in VC funding for his first startup. Vincent also created the first IoT platform to automate growth and content generation for digital publishers, using a system of API's for machine-to-machine communications, involving Hootsuite, Twitter, and Google Analytics.

Vincent's profile is accessible here and includes top publications, presentations, and work experience with Visa, Microsoft, eBay, NBC, Wells Fargo, and other organisations.

**Introduction**

To find out whether this book might be useful to you, read my introduction.

**Table of Content**

- Introduction xxi
- Chapter 1 What Is Data Science? 1
- Chapter 2 Big Data Is Different 41
- Chapter 3 Becoming a Data Scientist 73
- Chapter 4 Data Science Craftsmanship, Part I 109
- Chapter 5 Data Science Craftsmanship, Part II 151
- Chapter 6 Data Science Application Case Studies 195
- Chapter 7 Launching Your New Data Science Career 255
- Chapter 8 Data Science Resources 287
- Index 299

**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
- 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 and 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 Bit.ly 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

*Follow me on Twitter at @ROIdoctor*

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