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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Sanjiv Jaggia is a professor of economics and finance at California Polytechnic State University in San Luis Obispo. Dr. Jaggia holds a Ph.D. from Indiana University and is a Chartered Financial Analyst (CFA®). He enjoys research in statistics and data analytics applied to a wide range of business disciplines. Dr. Jaggia has published numerous papers in leading academic journals and has co-authored three successful textbooks, two in business statistics and one in business analytics. His ability to communicate in the classroom has been acknowledged by several teaching awards. Dr. Jaggia resides in San Luis Obispo with his wife and daughter. In his spare time, he enjoys cooking, hiking, and listening to a wide range of music.
Inhaltsangabe
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
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
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