Data Science for Business

What you need to know about data Mining and data analytic thinking.

DETAIL

• Author: Foster Provost and Tom Fawcett
• Language: English
• Published: 2013
• Page: 409
• Size: 16 MB
• Format: pdf

CONTENTS

Preface

1. Introduction: Data-Analytic Thinking
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.
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.
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.
5. Overfitting and Its Avoidance. Fundamental concepts: Generalization; Fitting and overfitting; Complexity control. Exemplary techniques: Cross-validation; Attribute selection; Tree pruning; Regularization.
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.
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.
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.
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.
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.
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.
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; Biasvariance decomposition of error; Ensembles of models; Causal reasoning from data.
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.
14. Conclusion

A. Proposal Review Guide.

B. Another Sample Proposal.

Glossary

Bibliography

Index

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