Our Conceptual Approach to Data Science
To the Instructor
Other Skills and Concepts
Sections and Notation
Some Notes on Grammar and Usage
Safari® Books Online
How to Contact Us
1. Introduction: Data-Analytic Thinking
The Ubiquity of Data Opportunities
Example: Hurricane Frances
Example: Predicting Customer Churn
Data Science, Engineering, and Data-Driven Decision Making
Data Processing and "Big Data"
From Big Data 1.0 to Big Data 2.0
Data and Data Science Capability as a Strategic Asset
Data Mining and Data Science, Revisited
Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist
2. Business Problems and Data Science Solutions
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
Supervised Versus Unsupervised Methods
Data Mining and Its Results
The Data Mining Process
Implications for Managing the Data Science Team
Other Analytics Techniques and Technologies
Machine Learning and Data Mining
Answering Business Questions with These Techniques
3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
Fundamental concepts: Identifying informative attributes; Segmenting data by progressive attribute selection.
Exemplary techniques: Finding correlations; Attribute/variable selection; Tree induction.
Models, Induction, and Prediction
Selecting Informative Attributes
Example: Attribute Selection with Information Gain
Supervised Segmentation with Tree-Structured Models
Trees as Sets of Rules
Example: Addressing the Churn Problem with Tree Induction
4. Fitting a Model to Data
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
Linear Discriminant Functions
Optimizing an Objective Function
An Example of Mining a Linear Discriminant from Data
Linear Discriminant Functions for Scoring and Ranking Instances
Support Vector Machines, Briefly
Regression via Mathematical Functions
Class Probability Estimation and Logistic ``Regression''
* Logistic Regression: Some Technical Details
Example: Logistic Regression versus Tree Induction
Nonlinear Functions, Support Vector Machines, and Neural Networks
5. Overfitting and Complexity Control
Fundamental concepts: Generalization; Fitting and overfitting; Complexity control.
Exemplary techniques: Cross-validation; Attribute selection; Tree pruning; Regularization.
Holdout Data and Fitting Graphs
Overfitting in Tree Induction
Overfitting in Mathematical Functions
Example: Overfitting Linear Functions
* Example: Why Is Overfitting Bad?
From Holdout Evaluation to Cross-Validation
The Churn Dataset Revisited
Overfitting Avoidance and Complexity Control
Avoiding Overfitting with Tree Induction
A General Method for Avoiding Overfitting
* Avoiding Overfitting for Parameter Optimization
6. Similarity, Neighbors, and Clusters
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
Example: Whiskey Analytics
Nearest Neighbors for Predictive Modeling
How Many Neighbors and How Much Influence?
Geometric Interpretation, Overfitting, and Complexity Control
Issues with Nearest-Neighbor Methods
Dimensionality and domain knowledge
Some Important Technical Details Relating to Similarities and Neighbors
* Other Distance Functions
* Combining Functions: Calculating Scores from Neighbors
Example: Whiskey Analytics Revisited
Nearest Neighbors Revisited: Clustering Around Centroids
Example: Clustering Business News Stories
The news story clusters
Understanding the Results of Clustering
* Using Supervised Learning to Generate Cluster Descriptions
Stepping Back: Solving a Business Problem Versus Data Exploration
7. Decision Analytic Thinking I: What Is a Good Model?
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.
Plain Accuracy and Its Problems
The Confusion Matrix
Problems with Unbalanced Classes
Problems with Unequal Costs and Benefits
Generalizing Beyond Classification
A Key Analytical Framework: Expected Value
Using Expected Value to Frame Classifier Use
Using Expected Value to Frame Classifier Evaluation
Costs and benefits
Evaluation, Baseline Performance, and Implications for Investments in Data
8. Visualizing Model Performance
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
ROC Graphs and Curves
The Area Under the ROC Curve (AUC)
Cumulative Response and Lift Curves
Example: Performance Analytics for Churn Modeling
9. Evidence and Probabilities
Fundamental concepts: Explicit evidence combination with Bayes' Rule; Probabilistic reasoning via assumptions of conditional independence.
Exemplary techniques: Naive Bayes classification; Evidence lift.
Example: Targeting Online Consumers With Advertisements
Combining Evidence Probabilistically
Joint Probability and Independence
Applying Bayes’ Rule to Data Science
Conditional Independence and Naive Bayes
Advantages and Disadvantages of Naive Bayes
A Model of Evidence ``Lift''
Example: Evidence Lifts from Facebook "Likes"
Evidence in Action: Targeting Consumers with Ads
10. Representing and Mining Text
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
Why Text Is Difficult
Bag of Words
Measuring Sparseness: Inverse Document Frequency
Combining Them: TFIDF
Example: Jazz Musicians
* The Relationship of IDF to Entropy
Beyond Bag of Words
Named Entity Extraction
Example: Mining News Stories to Predict Stock Price Movement
11. Decision Analytic Thinking II: Toward Analytical Engineering
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.
Targeting the Best Prospects for a Charity Mailing
The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces
A Brief Digression on Selection Bias
Our Churn Example Revisited with Even More Sophistication
The Expected Value Framework: Structuring a More Complicated Business Problem
Assessing the Influence of the Incentive
From an Expected Value Decomposition to a Data Science Solution
12. Other Data Science Tasks and Techniques
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; Bias-variance decomposition of error; Ensembles of models; Causal reasoning from data.
Co-occurrences and Associations: Finding Items That Go Together
Measuring Surprise: Lift and Leverage
Example: Beer and Lottery Tickets
Associations Among Facebook Likes
Profiling: Finding Typical Behavior
Link Prediction and Social Recommendation
Data Reduction, Latent Information, and Movie Recommendation
Bias, Variance, and Ensemble Methods
Data-Driven Causal Explanation and a Viral Marketing Example
13. Data Science and Business Strategy
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
Achieving Competitive Advantage with Data Science
Sustaining Competitive Advantage with Data Science
Formidable Historical Advantage
Unique Intellectual Property
Unique Intangible Collateral Assets
Superior Data Scientists
Superior Data Science Management
Attracting and Nurturing Data Scientists and Their Teams
Examine Data Science Case Studies
Be Ready to Accept Creative Ideas from Any Source
Be Ready to Evaluate Proposals for Data Science Projects
Example Data Mining Proposal
Flaws in the Big Red Proposal
A Firm's Data Science Maturity
The Fundamental Concepts of Data Science
Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data
Changing the Way We Think about Solutions to Business Problems
What Data Can't Do: Humans in the Loop, Revisited
Privacy, Ethics, and Mining Data About Individuals
Is There More to Data Science?
Final Example: From Crowd-Sourcing to Cloud-Sourcing
Appendix A. Proposal Review Guide
Business and Data Understanding
Evaluation and Deployment
Appendix B. Another Sample Proposal
Business and Data Understanding
Evaluation and Deployment
About the Authors
Most chapters introduce a set of fundamental concepts and exemplary techniques. Starred sections are optional and introduce advanced technical material.
Table of Contents