Business Analytics ISE

2nd Edition
1265087687 · 9781265087685
Business Analytics: Communicating with Numbers was written from the ground up to prepare students to understand, manage, and visualize the data, apply the appropriate tools, and communicate the findings and their relevance. Unlike other texts that si… Read More
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CHAPTER 1: Introduction to Business Analytics

CHAPTER 2: Data Management and Wrangling

CHAPTER 3: Summary Measures

CHAPTER 4: Data Visualization

CHAPTER 5: Probability and Probability Distributions

CHAPTER 6: Statistical Inference

CHAPTER 7: Regression Analysis

CHAPTER 8: Introduction to Data Mining

CHAPTER 9: More Topics in Regression Analysis

CHAPTER 10: Logistic Regression Models

CHAPTER 11: Supervised Data Mining: kNN and Naive Bayes

CHAPTER 12: Supervised Data Mining: Decision Trees

CHAPTER 13: Unsupervised Data Mining

CHAPTER 14: Forecasting with Time Series Data

CHAPTER 15: Spreadsheet Modelling

CHAPTER 16: Risk and Simulation

CHAPTER 17: Optimization: Linear Programming

CHAPTER 18: Optimization: Integer and Nonlinear Programming

APPENDIX A Big Data Sets: Variable Description and Data Dictionary
APPENDIX B Getting Started with Excel and Excel Add-Ins
APPENDIX C Getting Started with R
APPENDIX D Statistical Tables
APPENDIX E Answers to Selected Exercises
Business Analytics: Communicating with Numbers was written from the ground up to prepare students to understand, manage, and visualize the data, apply the appropriate tools, and communicate the findings and their relevance. Unlike other texts that simply repackage statistics and traditional operations research topics, this text seamlessly threads the topics of data wrangling, descriptive analytics, predictive analytics, and prescriptive analytics into a cohesive whole. It provides a holistic analytics process, including dealing with real life data that are not necessarily 'clean' and/or 'small' and stresses the importance of effectively communicating findings by including features such as a synopsis (a short writing sample) and a sample report (a longer writing sample) in every chapter. These features help students develop skills in articulating the business value of analytics by communicating insights gained from a non-technical standpoint.
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