Data Science for Business
What you need to know about data mining and data-analytic thinking
(Sprache: Englisch)
Data Science for Business is intended for those who need to understand data science/data mining, and those who want to develop their skill at data-analytic thinking. This is not a book about algorithms. Instead it presents a set of fundamental principles for extracting useful knowledge from data.
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Produktinformationen zu „Data Science for Business “
Klappentext zu „Data Science for Business “
Data Science for Business is intended for those who need to understand data science/data mining, and those who want to develop their skill at data-analytic thinking. This is not a book about algorithms. Instead it presents a set of fundamental principles for extracting useful knowledge from data.
Inhaltsverzeichnis zu „Data Science for Business “
- Praise
- Preface
- Chapter 1: Introduction: Data-Analytic Thinking
- Chapter 2: Business Problems and Data Science Solutions
- Chapter 3: Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
- Chapter 4: Fitting a Model to Data
- Chapter 5: Overfitting and Its Avoidance
- Chapter 6: Similarity, Neighbors, and Clusters
- Chapter 7: Decision Analytic Thinking I: What Is a Good Model?
- Chapter 8: Visualizing Model Performance
- Chapter 9: Evidence and Probabilities
- Chapter 10: Representing and Mining Text
- Chapter 11: Decision Analytic Thinking II: Toward Analytical Engineering
- Chapter 12: Other Data Science Tasks and Techniques
- Chapter 13: Data Science and Business Strategy
- Chapter 14: Conclusion
- Proposal Review Guide
- Another Sample Proposal
- Glossary
- Bibliography
- Index
- Colophon
Autoren-Porträt von Foster Provost, Tom Fawcett
Foster Provost is a Professor and NEC Faculty Fellow at the NYU Stern School of Business, where he has taught data science to MBAs for 15 years. His research and teaching focus on data science, machine learning, business analytics, (social) network data, and crowd-sourcing for data analytics. Tom Fawcett has a Ph.D. in machine learning from UMass-Amherst and has worked in industrial research (GTE Laboratories, NYNEX/Verizon Labs, HP Labs, etc.). He has served as action editor of the Machine Learning journal, before which he was an editorial board member.
Bibliographische Angaben
- Autoren: Foster Provost , Tom Fawcett
- 2015, 408 Seiten, mit Abbildungen, Maße: 17,9 x 23,3 cm, Kartoniert (TB), Englisch
- Verlag: O'Reilly Media
- ISBN-10: 1449361323
- ISBN-13: 9781449361327
Sprache:
Englisch
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