Data Science for Business. What You Need to Know about Data Mining and Data-Analytic Thinking

Spis treści książki

• Data Science for Business
• Dedication
• Dedication
• Preface
  • Our Conceptual Approach to Data Science
  • To the Instructor
  • Other Skills and Concepts
  • Sections and Notation
  • Using Examples
  • Safari Books Online
  • How to Contact Us
  • Acknowledgments
• 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-Analytic Thinking
  • This Book
  • Data Mining and Data Science, Revisited
  • Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist
  • Summary
• 2. Business Problems and Data Science Solutions
  • From Business Problems to Data Mining Tasks
  • Supervised Versus Unsupervised Methods
  • Data Mining and Its Results
  • The Data Mining Process
    • Business Understanding
    • Data Understanding
    • Data Preparation
    • Modeling
    • Evaluation
    • Deployment
  • Implications for Managing the Data Science Team
  • Other Analytics Techniques and Technologies
    • Statistics
    • Database Querying
    • Data Warehousing
    • Regression Analysis
    • Machine Learning and Data Mining
    • Answering Business Questions with These Techniques
  • Summary
• 3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
  • Models, Induction, and Prediction
  • Supervised Segmentation
    • Selecting Informative Attributes
    • Example: Attribute Selection with Information Gain
    • Supervised Segmentation with Tree-Structured Models
  • Visualizing Segmentations
  • Trees as Sets of Rules
  • Probability Estimation
  • Example: Addressing the Churn Problem with Tree Induction
  • Summary
• 4. Fitting a Model to Data
  • 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
  • Summary
• 5. Overfitting and Its Avoidance
  • Generalization
  • Overfitting
  • Overfitting Examined
    • 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
  • Learning Curves
  • Overfitting Avoidance and Complexity Control
    • Avoiding Overfitting with Tree Induction
    • A General Method for Avoiding Overfitting
    • * Avoiding Overfitting for Parameter Optimization
  • Summary
• 6. Similarity, Neighbors, and Clusters
  • Similarity and Distance
  • Nearest-Neighbor Reasoning
    • Example: Whiskey Analytics
    • Nearest Neighbors for Predictive Modeling
      • Classification
      • Probability Estimation
      • Regression
    • How Many Neighbors and How Much Influence?
    • Geometric Interpretation, Overfitting, and Complexity Control
    • Issues with Nearest-Neighbor Methods
      • Intelligibility
      • Dimensionality and domain knowledge
      • Computational efficiency
  • Some Important Technical Details Relating to Similarities and Neighbors
    • Heterogeneous Attributes
    • * Other Distance Functions
    • * Combining Functions: Calculating Scores from Neighbors
  • Clustering
    • Example: Whiskey Analytics Revisited
    • Hierarchical Clustering
    • Nearest Neighbors Revisited: Clustering Around Centroids
    • Example: Clustering Business News Stories
      • Data preparation
      • 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
  • Summary
• 7. Decision Analytic Thinking I: What Is a Good Model?
  • Evaluating Classifiers
    • 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
      • Error rates
      • Costs and benefits
  • Evaluation, Baseline Performance, and Implications for Investments in Data
  • Summary
• 8. Visualizing Model Performance
  • Ranking Instead of Classifying
  • Profit Curves
  • ROC Graphs and Curves
  • The Area Under the ROC Curve (AUC)
  • Cumulative Response and Lift Curves
  • Example: Performance Analytics for Churn Modeling
  • Summary
• 9. Evidence and Probabilities
  • Example: Targeting Online Consumers With Advertisements
  • Combining Evidence Probabilistically
    • Joint Probability and Independence
    • Bayes Rule
  • 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
  • Summary
• 10. Representing and Mining Text
  • Why Text Is Important
  • Why Text Is Difficult
  • Representation
    • Bag of Words
    • Term Frequency
    • Measuring Sparseness: Inverse Document Frequency
    • Combining Them: TFIDF
  • Example: Jazz Musicians
  • * The Relationship of IDF to Entropy
  • Beyond Bag of Words
    • N-gram Sequences
    • Named Entity Extraction
    • Topic Models
  • Example: Mining News Stories to Predict Stock Price Movement
    • The Task
    • The Data
    • Data Preprocessing
    • Results
  • Summary
• 11. Decision Analytic Thinking II: Toward Analytical Engineering
  • 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
    • Summary
• 12. Other Data Science Tasks and Techniques
  • 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
  • Summary
• 13. Data Science and Business Strategy
  • 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 Firms Data Science Maturity
• 14. Conclusion
  • 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 Cant 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
  • Final Words
• A. Proposal Review Guide
  • Business and Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation and Deployment
• B. Another Sample Proposal
  • Scenario and Proposal
    • Flaws in the GGC Proposal
• Glossary
• C. Bibliography
• Index
• Colophon
• Copyright

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