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Data science for business : what you need to know about data mining and data-analytic thinking

Author: Foster Provost; Tom Fawcett
Publisher: Sebastopol, CA : O'Reilly Media, 2013.
Edition/Format:   eBook : Document : English : 1st edView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Provost, Foster, 1964-
Data science for business.
Sebastopol, Calif. : O'Reilly, 2013
(OCoLC)844460899
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Foster Provost; Tom Fawcett
ISBN: 9781449374280 144937428X 9781449374297 1449374298
OCLC Number: 857306630
Description: 1 online resource (xviii, 384 pages) : illustrations
Contents: Machine generated contents note: 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 --
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 --
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 --
Fundamental concepts: Identifying informative attributes; Segmenting data by progressive attribute selection --
Exemplary techniques: Finding correlations; Attribute/variable selection; Tree induction --
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 --
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 --
Summary --
5. Overfitting and Its Avoidance --
Fundamental concepts: Generalization; Fitting and overfitting; Complexity control --
Exemplary techniques: Cross-validation; Attribute selection; Tree pruning; Regularization --
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 --
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 --
Nearest-Neighbor Reasoning --
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 --
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 --
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? --
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 --
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 --
Evaluation, Baseline Performance, and Implications for Investments in Data --
Summary --
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 --
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 --
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 --
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 --
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 --
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 --
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 --
Summary --
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 --
Summary --
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. Note continued: Flaws in the Big Red Proposal --
A Firm's 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 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 --
Final Words.
Responsibility: Foster Provost & Tom Fawcett.

Abstract:

This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge  Read more...

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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 -- 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 -- 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 -- Summary -- 5. Overfitting and Its Avoidance -- Fundamental concepts: Generalization; Fitting and overfitting; Complexity control -- Exemplary techniques: Cross-validation; Attribute selection; Tree pruning; Regularization -- 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. 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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 -- 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 -- Evaluation, Baseline Performance, and Implications for Investments in Data -- Summary -- 8. 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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 -- 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 -- 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 -- 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. 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