Modern Business Analytics ISE
Published on 9. May 2022
Book
Paperback/Softback
464 pages
978-1-266-10833-4 (ISBN)
Description
Written by Matt Taddy, successful author of the McGraw Hill Professional title, Business Data Science graduate of University of Chicago and Amazon Chief Economist. This new higher-ed text takes a practical, modern approach to data science and business analytics for the graduate-level business analytics student or professional. It takes a learn-by-doing approach, with real data analysis examples that explain the "why", rather than the "what" in the decision-making discussions. It uses R as the primary technology throughout the text and includes an end-of-chapter reference to the basic R recipes in each chapter. The text uses tools from economics and statistics in combination with Machine Learning Techniques to create a platform for using data to make decisions.
The Connect product that supports the text includes Interactive Activities that have students explore content more deeply, Excel activities like Integrated Excel & Applying Excel, and a Prep Course that helps students refresh on fundamental pre-requisite knowledge they need to know prior to this course.
The Connect product that supports the text includes Interactive Activities that have students explore content more deeply, Excel activities like Integrated Excel & Applying Excel, and a Prep Course that helps students refresh on fundamental pre-requisite knowledge they need to know prior to this course.
More details
Language
English
Place of publication
OH
United States
Target group
College/higher education
US School Grade: From College Freshman to College Graduate Student
Illustrations
400 Illustrations
Dimensions
Height: 251 mm
Width: 202 mm
Thickness: 18 mm
Weight
826 gr
ISBN-13
978-1-266-10833-4 (9781266108334)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Content
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
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