Data Science for Business

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Data Science for Business: What you need to know about data mining and data-analytic thinking
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1. Introduction: Data-Analytic Thinking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
The Ubiquity of Data Opportunities 1
Example: Hurricane Frances 3
Example: Predicting Customer Churn 4
Data Science, Engineering, and Data-Driven Decision Making 4
Data Processing and ¡°Big Data¡± 7
From Big Data 1.0 to Big Data 2.0 8
Data and Data Science Capability as a Strategic Asset 9
Data-Analytic Thinking 12
This Book 14
Data Mining and Data Science, Revisited 14
Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data
Scientist 15
Summary 16
2. Business Problems and Data Science Solutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Fundamental concepts: A set of canonical data mining tasks; The data mining process;
Supervised versus unsupervised data mining.
From Business Problems to Data Mining Tasks 19
Supervised Versus Unsupervised Methods 24
Data Mining and Its Results 25
The Data Mining Process 26
Business Understanding 27
Data Understanding 28
Data Preparation 29
Modeling 31
Evaluation 31
iii
Deployment 32
Implications for Managing the Data Science Team 34
Other Analytics Techniques and Technologies 35
Statistics 35
Database Querying 37
Data Warehousing 38
Regression Analysis 39
Machine Learning and Data Mining 39
Answering Business Questions with These Techniques 40
Summary 41
3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation. 43
Fundamental concepts: Identifying informative attributes; Segmenting data by
progressive attribute selection.
Exemplary techniques: Finding correlations; Attribute/variable selection; Tree
induction.
Models, Induction, and Prediction 44
Supervised Segmentation 48
Selecting Informative Attributes 49
Example: Attribute Selection with Information Gain 56
Supervised Segmentation with Tree-Structured Models 62
Visualizing Segmentations 67
Trees as Sets of Rules 71
Probability Estimation 71
Example: Addressing the Churn Problem with Tree Induction 73
Summary 78
4. Fitting a Model to Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Fundamental concepts: Finding ¡°optimal¡± model parameters based on data; Choosing
the goal for data mining; Objective functions; Loss functions.
Exemplary techniques: Linear regression; Logistic regression; Support-vector machines.
Classification via Mathematical Functions 83
Linear Discriminant Functions 85
Optimizing an Objective Function 87
An Example of Mining a Linear Discriminant from Data 88
Linear Discriminant Functions for Scoring and Ranking Instances 90
Support Vector Machines, Briefly 91
Regression via Mathematical Functions 94
Class Probability Estimation and Logistic ¡°Regression¡± 96
* Logistic Regression: Some Technical Details 99
Example: Logistic Regression versus Tree Induction 102
Nonlinear Functions, Support Vector Machines, and Neural Networks 105
iv | Table of Contents
Summary 108
5. Overfitting and Its Avoidance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Fundamental concepts: Generalization; Fitting and overfitting; Complexity control.
Exemplary techniques: Cross-validation; Attribute selection; Tree pruning;
Regularization.
Generalization 111
Overfitting 113
Overfitting Examined 113
Holdout Data and Fitting Graphs 113
Overfitting in Tree Induction 116
Overfitting in Mathematical Functions 118
Example: Overfitting Linear Functions 119
* Example: Why Is Overfitting Bad? 124
From Holdout Evaluation to Cross-Validation 126
The Churn Dataset Revisited 129
Learning Curves 130
Overfitting Avoidance and Complexity Control 133
Avoiding Overfitting with Tree Induction 133
A General Method for Avoiding Overfitting 134
* Avoiding Overfitting for Parameter Optimization 136
Summary 140
6. Similarity, Neighbors, and Clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Fundamental concepts: Calculating similarity of objects described by data; Using
similarity for prediction; Clustering as similarity-based segmentation.
Exemplary techniques: Searching for similar entities; Nearest neighbor methods;
Clustering methods; Distance metrics for calculating similarity.
Similarity and Distance 142
Nearest-Neighbor Reasoning 144
Example: Whiskey Analytics 144
Nearest Neighbors for Predictive Modeling 146
How Many Neighbors and How Much Influence? 149
Geometric Interpretation, Overfitting, and Complexity Control 151
Issues with Nearest-Neighbor Methods 154
Some Important Technical Details Relating to Similarities and Neighbors 157
Heterogeneous Attributes 157
* Other Distance Functions 158
* Combining Functions: Calculating Scores from Neighbors 161
Clustering 163
Example: Whiskey Analytics Revisited 163
Hierarchical Clustering 164
Table of Contents | v
Nearest Neighbors Revisited: Clustering Around Centroids 169
Example: Clustering Business News Stories 174
Understanding the Results of Clustering 177
* Using Supervised Learning to Generate Cluster Descriptions 179
Stepping Back: Solving a Business Problem Versus Data Exploration 182
Summary 184
7. Decision Analytic Thinking I: What Is a Good Model?. . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Fundamental concepts: Careful consideration of what is desired from data science
results; Expected value as a key evaluation framework; Consideration of appropriate
comparative baselines.
Exemplary techniques: Various evaluation metrics; Estimating costs and benefits;
Calculating expected profit; Creating baseline methods for comparison.
Evaluating Classifiers 188
Plain Accuracy and Its Problems 189
The Confusion Matrix 189
Problems with Unbalanced Classes 190
Problems with Unequal Costs and Benefits 193
Generalizing Beyond Classification 193
A Key Analytical Framework: Expected Value 194
Using Expected Value to Frame Classifier Use 195
Using Expected Value to Frame Classifier Evaluation 196
Evaluation, Baseline Performance, and Implications for Investments in Data 204
Summary 207
8. Visualizing Model Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Fundamental concepts: Visualization of model performance under various kinds of
uncertainty; Further consideration of what is desired from data mining results.
Exemplary techniques: Profit curves; Cumulative response curves; Lift curves; ROC
curves.
Ranking Instead of Classifying 209
Profit Curves 212
ROC Graphs and Curves 214
The Area Under the ROC Curve (AUC) 219
Cumulative Response and Lift Curves 219
Example: Performance Analytics for Churn Modeling 223
Summary 231
9. Evidence and Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Fundamental concepts: Explicit evidence combination with Bayes¡ˉ Rule; Probabilistic
reasoning via assumptions of conditional independence.
Exemplary techniques: Naive Bayes classification; Evidence lift.
vi | Table of Contents
Example: Targeting Online Consumers With Advertisements 233
Combining Evidence Probabilistically 235
Joint Probability and Independence 236
Bayes¡ˉ Rule 237
Applying Bayes¡ˉ Rule to Data Science 239
Conditional Independence and Naive Bayes 240
Advantages and Disadvantages of Naive Bayes 242
A Model of Evidence ¡°Lift¡± 244
Example: Evidence Lifts from Facebook ¡°Likes¡± 245
Evidence in Action: Targeting Consumers with Ads 247
Summary 247
10. Representing and Mining Text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
Fundamental concepts: The importance of constructing mining-friendly data
representations; Representation of text for data mining.
Exemplary techniques: Bag of words representation; TFIDF calculation; N-grams;
Stemming; Named entity extraction; Topic models.
Why Text Is Important 250
Why Text Is Difficult 250
Representation 251
Bag of Words 252
Term Frequency 252
Measuring Sparseness: Inverse Document Frequency 254
Combining Them: TFIDF 256
Example: Jazz Musicians 256
* The Relationship of IDF to Entropy 261
Beyond Bag of Words 263
N-gram Sequences 263
Named Entity Extraction 264
Topic Models 264
Example: Mining News Stories to Predict Stock Price Movement 266
The Task 266
The Data 268
Data Preprocessing 270
Results 271
Summary 275
11. Decision Analytic Thinking II: Toward Analytical Engineering. . . . . . . . . . . . . . . . . . . . 277
Fundamental concept: Solving business problems with data science starts with
analytical engineering: designing an analytical solution, based on the data, tools, and
techniques available.
Exemplary technique: Expected value as a framework for data science solution design.
Table of Contents | vii
Targeting the Best Prospects for a Charity Mailing 278
The Expected Value Framework: Decomposing the Business Problem and
Recomposing the Solution Pieces 278
A Brief Digression on Selection Bias 280
Our Churn Example Revisited with Even More Sophistication 281
The Expected Value Framework: Structuring a More Complicated Business
Problem 281
Assessing the Influence of the Incentive 283
From an Expected Value Decomposition to a Data Science Solution 284
Summary 287
12. Other Data Science Tasks and Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
Fundamental concepts: Our fundamental concepts as the basis of many common data
science techniques; The importance of familiarity with the building blocks of data
science.
Exemplary techniques: Association and co-occurrences; Behavior profiling; Link
prediction; Data reduction; Latent information mining; Movie recommendation; Biasvariance
decomposition of error; Ensembles of models; Causal reasoning from data.
Co-occurrences and Associations: Finding Items That Go Together 290
Measuring Surprise: Lift and Leverage 291
Example: Beer and Lottery Tickets 292
Associations Among Facebook Likes 293
Profiling: Finding Typical Behavior 296
Link Prediction and Social Recommendation 301
Data Reduction, Latent Information, and Movie Recommendation 302
Bias, Variance, and Ensemble Methods 306
Data-Driven Causal Explanation and a Viral Marketing Example 309
Summary 310
13. Data Science and Business Strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Fundamental concepts: Our principles as the basis of success for a data-driven
business; Acquiring and sustaining competitive advantage via data science; The
importance of careful curation of data science capability.
Thinking Data-Analytically, Redux 313
Achieving Competitive Advantage with Data Science 315
Sustaining Competitive Advantage with Data Science 316
Formidable Historical Advantage 317
Unique Intellectual Property 317
Unique Intangible Collateral Assets 318
Superior Data Scientists 318
Superior Data Science Management 320
Attracting and Nurturing Data Scientists and Their Teams 321
viii | Table of Contents
Examine Data Science Case Studies 323
Be Ready to Accept Creative Ideas from Any Source 324
Be Ready to Evaluate Proposals for Data Science Projects 324
Example Data Mining Proposal 325
Flaws in the Big Red Proposal 326
A Firm¡ˉs Data Science Maturity 327
14. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
The Fundamental Concepts of Data Science 331
Applying Our Fundamental Concepts to a New Problem: Mining Mobile
Device Data 334
Changing the Way We Think about Solutions to Business Problems 337
What Data Can¡ˉt Do: Humans in the Loop, Revisited 338
Privacy, Ethics, and Mining Data About Individuals 341
Is There More to Data Science? 342
Final Example: From Crowd-Sourcing to Cloud-Sourcing 343
Final Words 344
A. Proposal Review Guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
B. Another Sample Proposal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367

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