Book description
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces 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. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates
Publisher resources
Table of contents
- Praise
- Dedication
- Preface
-
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
- 3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
- 4. Fitting a Model to Data
- 5. Overfitting and Its Avoidance
-
6. Similarity, Neighbors, and Clusters
- Similarity and Distance
- Nearest-Neighbor Reasoning
- Some Important Technical Details Relating to Similarities and Neighbors
- Clustering
- Stepping Back: Solving a Business Problem Versus Data Exploration
- Summary
- 7. Decision Analytic Thinking I: What Is a Good Model?
- 8. Visualizing Model Performance
- 9. Evidence and Probabilities
- 10. Representing and Mining Text
- 11. Decision Analytic Thinking II: Toward Analytical Engineering
-
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
- Summary
-
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
- A. Proposal Review Guide
- B. Another Sample Proposal
- Glossary
- C. Bibliography
- Index
- Colophon
- Copyright
Product information
- Title: Data Science for Business
- Author(s):
- Release date: August 2013
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781449361327
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