Modern business analytics : practical data science for decision-making / Matt Taddy, Amazon, Inc., Leslie Hendrix, University of South Carolina, Matthew Harding, University of California, Irvine.

By: Taddy, Matt [author.]
Contributor(s): Hendrix, Leslie [author.] | Harding, Matthew, 1974- [author.]
Language: English Publisher: New York, NY : McGraw Hill Education, [2023] ©2023Description: xxii, 442 pages : illustrations; 26 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781266108334Subject(s): Decision making -- Econometric models | Machine learningDDC classification: 658.4/03 LOC classification: HD30.23 | .T3245 2023
Contents:
Chapter 1: Regression Chapter 2: Uncertainty Quantification Chapter 3: Regularization and Selection Chapter 4: Classification Chapter 5: Causal Inference with Experiments Chapter 6: Causal Inference with Controls Chapter 7: Trees and Forests Chapter 8: Factor Models Chapter 9: Text as Data Chapter 10: Deep Learning Appendix: R Primer
Summary: "The practice of data analytics is changing and modernizing. Innovations in computation and machine learning are creating new opportunities for the data analyst: exposing previously unexplored data to scientific analysis, scaling tasks through automation, and allowing deeper and more accurate modeling"-- Provided by publisher.
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About the Author
Matt Taddy

Leslie Hendrix

Leslie Hendrix is a clinical associate professor in the Darla Moore School of Business at the University of South Carolina. She received her PhD in statistics in 2011 and a BS in mathematics in 2005 from the University of South Carolina. She has received two university-wide teaching awards for her work in teaching business analytics and statistics courses and is active in the research and teaching communities for analytics. She was instrumental in founding the Moore School’s newly formed Data Lab and currently serves as the assistant director.

Matthew Harding

Matthew C. Harding is a professor of economics and statistics at the University of California, Irvine. He holds a PhD from MIT and an M.Phil. from Oxford University. Dr. Harding conducts research on econometrics, consumer finance, health policy, and energy economics and has published widely in leading academic journals. He is the founder of Ecometricx, LLC, a big data and machine learning consulting company, and cofounder of FASTlab.global Institute, a nonprofit focusing on education and evidence-based policies in the areas of fair access and sustainable technologies.

Includes bibliographical references and index.

Chapter 1: Regression
Chapter 2: Uncertainty Quantification
Chapter 3: Regularization and Selection
Chapter 4: Classification
Chapter 5: Causal Inference with Experiments
Chapter 6: Causal Inference with Controls
Chapter 7: Trees and Forests
Chapter 8: Factor Models
Chapter 9: Text as Data
Chapter 10: Deep Learning
Appendix: R Primer

"The practice of data analytics is changing and modernizing. Innovations in computation and machine learning are creating new opportunities for the data analyst: exposing previously unexplored data to scientific analysis, scaling tasks through automation, and allowing deeper and more accurate modeling"-- Provided by publisher.

Ages 18+ McGraw Hill Education.

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