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Business Analytics, 1st Edition
ISBN10: 1260785009 | ISBN13: 9781260785005
© 2021
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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.
Ch. 1 Introduction to Business Analytics
Ch. 2 Data Management and Wrangling
Ch. 3 Data Visualization and Summary Measures
Ch. 4 Probability and Probability Distributions
Ch. 5 Statistical Inference
Ch. 6 Regression Analysis
Ch. 7 Advanced Regression Analysis
Ch. 8 Introduction to Data Mining
Ch. 9 Supervised Data Mining: k-Nearest Neighbors and Naïve Bayes
Ch. 10 Supervised Data Mining: Decision Trees
Ch. 11 Unsupervised Data Mining
Ch. 12 Forecasting with Time Series Data
Ch. 13 Introduction to Prescriptive Analytics
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
Ch. 2 Data Management and Wrangling
Ch. 3 Data Visualization and Summary Measures
Ch. 4 Probability and Probability Distributions
Ch. 5 Statistical Inference
Ch. 6 Regression Analysis
Ch. 7 Advanced Regression Analysis
Ch. 8 Introduction to Data Mining
Ch. 9 Supervised Data Mining: k-Nearest Neighbors and Naïve Bayes
Ch. 10 Supervised Data Mining: Decision Trees
Ch. 11 Unsupervised Data Mining
Ch. 12 Forecasting with Time Series Data
Ch. 13 Introduction to Prescriptive Analytics
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
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