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Data science for business : what you need to know about data mining and data-analytic thinking /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2013.
|
| Edición: | 1st ed. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Provost, Foster, |d 1964- | |
| 245 | 1 | 0 | |a Data science for business : |b what you need to know about data mining and data-analytic thinking / |c Foster Provost & Tom Fawcett. |
| 250 | |a 1st ed. | ||
| 260 | |a Sebastopol, CA : |b O'Reilly Media, |c 2013. | ||
| 300 | |a 1 online resource (xviii, 384 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references (pages 359-366) and index. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on April 02, 2019). | |
| 505 | 0 | |a 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. | |
| 505 | 0 | |a 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. | |
| 505 | 0 | |a 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. | |
| 520 | 8 | |a Annotation |b 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 and business value from the data you collect. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Information science. | |
| 650 | 0 | |a Business |x Data processing. | |
| 650 | 0 | |a Commerce. | |
| 650 | 2 | |a Data Mining | |
| 650 | 2 | |a Information Science | |
| 650 | 2 | |a Commerce | |
| 650 | 2 | |a Electronic Data Processing | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Sciences de l'information. | |
| 650 | 6 | |a Gestion |x Informatique. | |
| 650 | 6 | |a Commerce. | |
| 650 | 6 | |a Informatique. | |
| 650 | 7 | |a information science. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Database Management |x Data Warehousing. |2 bisacsh | |
| 650 | 7 | |a Sciences de l'information. |2 eclas | |
| 650 | 7 | |a Commerce |2 fast | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Business |x Data processing |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Information science |2 fast | |
| 650 | 7 | |a Data Mining |2 gnd | |
| 650 | 7 | |a Big Data |2 gnd | |
| 650 | 7 | |a Business Intelligence |2 gnd | |
| 650 | 7 | |a Data mining. |2 nli | |
| 650 | 7 | |a Big data. |2 nli | |
| 650 | 7 | |a Business |x Data processing. |2 nli | |
| 700 | 1 | |a Fawcett, Tom. | |
| 776 | 0 | 8 | |i Print version: |a Provost, Foster, 1964- |t Data science for business. |d Sebastopol, Calif. : O'Reilly, 2013 |z 1449361323 |z 9781449361327 |w (OCoLC)844460899 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781449374273/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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| 938 | |a YBP Library Services |b YANK |n 10906129 | ||
| 938 | |a Internet Archive |b INAR |n datascienceforbu0000prov | ||
| 994 | |a 92 |b IZTAP | ||