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| Preface | |
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| Introduction: Data-Analytic Thinking | |
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| The Ubiquity of Data Opportunities | |
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| Example: Hurricane Frances | |
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| Example: Predicting Customer Churn | |
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| Data Science, Engineering, and Data-Driven Decision Making | |
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| Data Processing and "Big Data" | |
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| From Big Data 1.0 to Big Data 2.0 | |
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| Data and Data Science Capability as a Strategic Asset | |
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| Data-Analytic Thinking | |
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| This Book | |
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| Data Mining and Data Science, Revisited | |
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| Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist | |
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| Summary | |
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| Business Problems and Data Science Solutions | |
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| Fundamental concepts: A set of canonical data mining tasks; The data mining process; Supervised versus unsupervised data mining. | |
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| From Business Problems to Data Mining Tasks | |
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| Supervised Versus Unsupervised Methods | |
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| Data Mining and Its Results | |
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| The Data Mining Process | |
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| Business Understanding | |
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| Data Understanding | |
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| Data Preparation | |
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| Modeling | |
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| Evaluation | |
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| Deployment | |
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| Implications for Managing the Data Science Team | |
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| Other Analytics Techniques and Technologies | |
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| Statistics | |
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| Database Querying | |
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| Data Warehousing | |
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| Regression Analysis | |
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| Machine Learning and Data Mining | |
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| Answering Business Questions with These Techniques | |
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| Summary | |
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| Introduction to Predictive Modeling: From Correlation to Supervised Segmentation. | |
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| Fundamental concepts: Identifying informative attributes; Segmenting data by progressive attribute selection. | |
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| Exemplary techniques: Finding correlations; Attribute/variable selection; Tree induction. | |
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| Models, Induction, and Prediction | |
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| Supervised Segmentation | |
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| Selecting Informative Attributes | |
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| Example: Attribute Selection with Information Gain | |
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| Supervised Segmentation with Tree-Structured Models | |
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| Visualizing Segmentations | |
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| Trees as Sets of Rules | |
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| Probability Estimation | |
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| Example: Addressing the Churn Problem with Tree Induction | |
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| Summary | |
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| Fitting a Model to Data | |
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| Fundamental concepts: Finding "optimal" model parameters based on data; Choosing the goal for data mining; Objective functions; Loss functions. | |
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| Exemplary techniques: Linear regression; Logistic regression; Support-vector machines. | |
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| Classification via Mathematical Functions | |
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| Linear Discriminant Functions | |
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| Optimizing an Objective Function | |
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| An Example of Mining a Linear Discriminant from Data | |
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| Linear Discriminant Functions for Scoring and Ranking Instances | |
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| Support Vector Machines, Briefly | |
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| Regression via Mathematical Functions | |
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| Class Probability Estimation and Logistic "Regression" | |
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| Logistic Regression: Some Technical Details | |
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| Example: Logistic Regression versus Tree Induction | |
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| Nonlinear Functions, Support Vector Machines, and Neural Networks | |
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| Summary | |
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| Overfitting and Its Avoidance | |
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| Fundamental concepts: Generalization; Fitting and overfitting; Complexity control. Exemplary techniques: Cross-validation; Attribute selection; Tree pruning; Regularization. | |
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| Generalization | |
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| Overfitting | |
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| Overfitting Examined | |
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| Holdout Data and Fitting Graphs | |
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| Overfitting in Tree Induction | |
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| Overfitting in Mathematical Functions | |
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| Example: Overfitting Linear Functions | |
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| Example: Why Is Overfitting Bad? | |
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| From Holdout Evaluation to Cross-Validation | |
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| The Churn Dataset Revisited | |
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| Learning Curves | |
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| Overfitting Avoidance and Complexity Control | |
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| Avoiding Overfitting with Tree Induction | |
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| A General Method for Avoiding Overfitting | |
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| Avoiding Overfitting for Parameter Optimization | |
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| Summary | |
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| Similarity, Neighbors, and Clusters | |
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| Fundamental concepts: Calculating similarity of objects described by data; Using similarity for prediction; Clustering as similarity-based segmentation. | |
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| Exemplary techniques: Searching for similar entities; Nearest neighbor methods; Clustering methods; Distance metrics for calculating similarity. | |
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| Similarity and Distance | |
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| Nearest-Neighbor Reasoning | |
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| Example: Whiskey Analytics | |
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| Nearest Neighbors for Predictive Modeling | |
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| How Many Neighbors and How Much Influence? | |
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| Geometric Interpretation, Overfitting, and Complexity Control | |
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| Issues with Nearest-Neighbor Methods | |
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| Some Important Technical Details Relating to Similarities and Neighbors | |
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| Heterogeneous Attributes | |
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| Other Distance Functions | |
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| Combining Functions: Calculating Scores from Neighbors | |
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| Clustering | |
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| Example: Whiskey Analytics Revisited | |
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| Hierarchical Clustering | |
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| Nearest Neighbors Revisited: Clustering Around Centroids | |
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| Example: Clustering Business News Stories | |
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| Understanding the Results of Clustering | |
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| Using Supervised Learning to Generate Cluster Descriptions | |
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| Stepping Back: Solving a Business Problem Versus Data Exploration | |
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| Summary | |
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| Decision Analytic Thinking I: What Is a Good Model? | |
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| Fundamental concepts: Careful consideration of what is desired from data science results; Expected value as a key evaluation framework; Consideration of appropriate comparative baselines. | |
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| Exemplary techniques: Various evaluation metrics; Estimating costs and benefits; Calculating expected profit; Creating baseline methods for comparison. | |
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| Evaluating Classifiers | |
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| Plain Accuracy and Its Problems | |
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| The Confusion Matrix | |
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| Problems with Unbalanced Classes | |
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| Problems with Unequal Costs and Benefits | |
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| Generalizing Beyond Classification | |
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| A Key Analytical Framework: Expected Value | |
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| Using Expected Value to Frame Classifier Use | |
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| Using Expected Value to Frame Classifier Evaluation | |
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| Evaluation, Baseline Performance, and Implications for Investments in Data | |
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| Summary | |
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| Visualizing Model Performance | |
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| Fundamental concepts: Visualization of model performance under various kinds of uncertainty; Further consideration of what is desired from data mining results. | |
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| Exemplary techniques: Profit curves; Cumulative response curves; Lift curves; ROC curves. | |
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| Ranking Instead of Classifying | |
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| Profit Curves | |
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| ROC Graphs and Curves | |
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| The Area Under the ROC Curve (AUC) | |
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| Cumulative Response and Lift Curves | |
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| Example: Performance Analytics for Churn Modeling | |
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| Summary | |
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| Evidence and Probabilities | |
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| Fundamental concepts: Explicit evidence combination with Bayes' Rule; Probabilistic reasoning via assumptions of conditional independence. | |
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| Exemplary techniques: Naive Bayes classification; Evidence lift. | |
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| Example: Targeting Online Consumers With Advertisements | |
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| Combining Evidence Probabilistically | |
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| Joint Probability and Independence | |
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| Bayes' Rule | |
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| Applying Bayes' Rule to Data Science | |
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| Conditional Independence and Naive Bayes | |
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| Advantages and Disadvantages of Naive Bayes | |
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| A Model of Evidence "Lift" | |
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| Example: Evidence Lifts from Facebook "Likes" | |
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| Evidence in Action: Targeting Consumers with Ads | |
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| Summary | |
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| Representing and Mining Text | |
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| Fundamental concepts: The importance of constructing mining-friendly data representations; Representation of text for data mining. | |
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| Exemplary techniques: Bag of words representation; TFIDF calculation; N-grams; Stemming; Named entity extraction; Topic models. | |
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| Why Text Is Important | |
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| Why Text Is Difficult | |
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| Representation | |
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| Bag of Words | |
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| Term Frequency | |
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| Measuring Sparseness: Inverse Document Frequency | |
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| Combining Them: TFIDF | |
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| Example: Jazz Musicians | |
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| The Relationship of IDF to Entropy | |
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| Beyond Bag of Words | |
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| N-gram Sequences | |
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| Named Entity Extraction | |
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| Topic Models | |
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| Example: Mining News Stories to Predict Stock Price Movement | |
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| The Task | |
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| The Data | |
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| Data Preprocessing | |
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| Results | |
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| Summary | |
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| Decision Analytic Thinking II: Toward Analytical Engineering | |
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| Fundamental concept: Solving business problems with data science starts with analytical engineering: designing an analytical solution, based on the data, tools, and techniques available. | |
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| Exemplary technique: Expected value as a framework for data science solution design. | |
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| Targeting the Best Prospects for a Charity Mailing | |
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| The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces | |
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| A Brief Digression on Selection Bias | |
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| Our Churn Example Revisited with Even More Sophistication | |
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| The Expected Value Framework: Structuring a More Complicated Business Problem | |
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| Assessing the Influence of the Incentive | |
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| From an Expected Value Decomposition to a Data Science Solution | |
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| Summary | |
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| Other Data Science Tasks and Techniques | |
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| 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. | |
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| 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. | |
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| Co-occurrences and Associations: Finding Items That Go Together | |
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| Measuring Surprise: Lift and Leverage | |
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| Example: Beer and Lottery Tickets | |
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| Associations Among Facebook Likes | |
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| Profiling: Finding Typical Behavior | |
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| Link Prediction and Social Recommendation | |
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| Data Reduction, Latent Information, and Movie Recommendation | |
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| Bias, Variance, and Ensemble Methods | |
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| Data-Driven Causal Explanation and a Viral Marketing Example | |
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| Summary | |
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| Data Science and Business Strategy | |
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| 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. | |
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| Thinking Data-Analytically, Redux | |
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| Achieving Competitive Advantage with Data Science | |
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| Sustaining Competitive Advantage with Data Science | |
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| Formidable Historical Advantage | |
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| Unique Intellectual Property | |
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| Unique Intangible Collateral Assets | |
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| Superior Data Scientists | |
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| Superior Data Science Management | |
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| Attracting and Nurturing Data Scientists and Their Teams | |
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| Examine Data Science Case Studies | |
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| Be Ready to Accept Creative Ideas from Any Source | |
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| Be Ready to Evaluate Proposals for Data Science Projects | |
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| Example Data Mining Proposal | |
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| Flaws in the Big Red Proposal | |
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| A Firm's Data Science Maturity | |
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| Conclusion | |
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| The Fundamental Concepts of Data Science | |
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| Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data | |
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| Changing the Way We Think about Solutions to Business Problems | |
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| What Data Can't Do: Humans in the Loop, Revisited | |
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| Privacy, Ethics, and Mining Data About Individuals | |
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| Is There More to Data Science? | |
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| Final Example: From Crowd-Sourcing to Cloud-Sourcing | |
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| Final Words | |
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| Proposal Review Guide | |
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| Another Sample Proposal | |
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| Glossary | |
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| Bibliography | |
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| Index | |