Data science for business : [what you need to know about data mining and data-analytic thinking] [electronic resource] / Foster Provost and Tom Fawcett.

By:

Provost, Foster

Contributor(s):

Fawcett, Tom

Material type:

TextPublication details: Beijing, etc. : O'Reilly, 2013.Description: 1 online resource (xxi, 386 p.) : ill. b&w and colContent type:

text

ISBN:

9781449374297

Subject(s):

DDC classification:

658.4038
006.312

Online resources:

Click here to view

Contents:

Ch. 1. Introduction: Data-analytic thinking -- Ch. 2. Business problems and data science solutions -- Ch. 3. Introduction to predictive modelling: from correlation to supervised segmentation -- Ch. 4. Fitting a model to data -- Ch. 5. Overfitting and its avoidance -- Ch. 6. Similarity, neighbours, and clusters -- Ch. 7. Decision analytic thinking I: What is a good model? -- Ch. 8. Visualizing model performance -- Ch. 9. Evidence and probabilities -- Ch. 10. Representing and mining text -- Ch. 11. Decision analytic thinking II: Toward analytical engineering -- Ch. 12. Other data science tasks and techniques -- Ch. 13. Data science and business strategy -- Ch. 14. Conclusion -- Appendix A. Proposal review guide -- Appendix B. Another sample proposal.

Summary: 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.

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Item type	Current library	Call number	Status	Notes	Barcode
Electronic resources	Marbella International University Centre	658.4038 PRO dat (Browse shelf(Opens below))	Available	E-book	EBC1323973

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Includes bibliographical references and index.

Ch. 1. Introduction: Data-analytic thinking --
Ch. 2. Business problems and data science solutions --
Ch. 3. Introduction to predictive modelling: from correlation to supervised segmentation --
Ch. 4. Fitting a model to data --
Ch. 5. Overfitting and its avoidance --
Ch. 6. Similarity, neighbours, and clusters --
Ch. 7. Decision analytic thinking I: What is a good model? --
Ch. 8. Visualizing model performance --
Ch. 9. Evidence and probabilities --
Ch. 10. Representing and mining text --
Ch. 11. Decision analytic thinking II: Toward analytical engineering --
Ch. 12. Other data science tasks and techniques --
Ch. 13. Data science and business strategy --
Ch. 14. Conclusion --
Appendix A. Proposal review guide --
Appendix B. Another sample proposal.

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.

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