Taddy, Matt, <br/><br/>
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. 
 - xxii, 442 pages :  illustrations;   26 cm 
<br/><br/> About the Author<br/>Matt Taddy<br/><br/>Leslie Hendrix<br/><br/>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.<br/><br/>Matthew Harding<br/><br/>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. 
<br/><br/>Includes bibliographical references and index. 
<br/><br/>Chapter 1: Regression <br/>Chapter 2: Uncertainty Quantification <br/>Chapter 3: Regularization and Selection <br/>Chapter 4: Classification <br/>Chapter 5: Causal Inference with Experiments <br/>Chapter 6: Causal Inference with Controls <br/>Chapter 7: Trees and Forests <br/>Chapter 8: Factor Models <br/>Chapter 9: Text as Data <br/>Chapter 10: Deep Learning <br/>Appendix: R Primer  
<br/><br/> "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"-- 
<br/><br/> Ages 18+  McGraw Hill Education. 
<br/><br/><label>ISBN: </label>9781266108334 
<br/><br/><label>LCCN: </label>2021058431 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Decision making--Econometric models.<br/>Machine learning.
<br/><br/><label>LC Class. No.: </label>HD30.23  / .T3245 2023 
<br/><br/><label>Dewey Class. No.: </label>658.4/03