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Data Sciences for Business

Paperback Engels 2013 9781449361327
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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.


Aantal pagina's:408
Hoofdrubriek:IT-management / ICT


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Over Foster Provost

Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business, where he teaches in the MBA, Business Analytics, and Data Science programs. Former Editor-in-Chief for the journal Machine Learning, Professor Provost has co-founded several successful companies focusing on data science for marketing.

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Over Tom Fawcett

Tom Fawcett holds a Ph.D. in machine learning and has worked in industry R&D for more than two decades for companies such as GTE Laboratories, NYNEX/Verizon Labs, and HP Labs. His published work has become standard reading in data science both on methodology (evaluating data mining results) and on applications (fraud detection and spam filtering).

Andere boeken door Tom Fawcett



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

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
-Implications for Managing the Data Science Team
-Other Analytics Techniques and Technologies

3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
-Models, Induction, and Prediction
-Supervised Segmentation
-Visualizing Segmentations
-Trees as Sets of Rules
-Probability Estimation
-Example: Addressing the Churn Problem with Tree Induction

4. Fitting a Model to Data
-Classification via Mathematical Functions
-Regression via Mathematical Functions
-Class Probability Estimation and Logistic 'Regression'
-Example: Logistic Regression versus Tree Induction
-Nonlinear Functions, Support Vector Machines, and Neural Networks

5. Overfitting and Its Avoidance
-Overfitting Examined
-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

6. Similarity, Neighbors, and Clusters
-Similarity and Distance
-Nearest-Neighbor Reasoning
-Some Important Technical Details Relating to Similarities and Neighbors
-Stepping Back: Solving a Business Problem Versus Data Exploration

7. Decision Analytic Thinking I: What Is a Good Model?
-Evaluating Classifiers
-Generalizing Beyond Classification
-A Key Analytical Framework: Expected Value
-Evaluation, Baseline Performance, and Implications for Investments in Data

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: churnperformance analytics for modeling performance analytics, for modeling churn Performance Analytics for Churn Modeling

9. Evidence and Probabilities
-Example: Targeting Online Consumers With Advertisements
-Combining Evidence Probabilistically
-Applying Bayes' Rule to Data Science
-A Model of Evidence 'Lift'
-Example: Evidence Lifts from Facebook "Likes"

10. Representing and Mining Text
-Why Text Is Important
-Why Text Is Difficult
-Example: Jazz Musicians
-* The Relationship of IDF to Entropy
-Beyond Bag of Words
-Example: Mining News Stories to Predict Stock Price Movement

11. Decision Analytic Thinking II: Toward Analytical Engineering
-Targeting the Best Prospects for a Charity Mailing
-Our Churn Example Revisited with Even More Sophistication

12. Other Data Science Tasks and Techniques
-Co-occurrences and Associations: Finding Items That Go Together
-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
-Thinking Data-Analytically, Redux
-Achieving Competitive Advantage with Data Science
-Sustaining Competitive Advantage with Data Science
-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
-A Firm's Data Science Maturity

14. Conclusion
-The Fundamental Concepts of Data Science
-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

Appendix A: Proposal Review Guide
Appendix B: Another Sample Proposal
Appendix C: Bibliography


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