My Account Details
ISBN10: 1264302800 | ISBN13: 9781264302802
ISBN10: 1264302800
ISBN13: 9781264302802
* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.
Instructor Information
Quick Actions (Only for Validated Instructor Accounts):
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.
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: More Topics in Regression Analysis
Chapter 9: Logistic Regression
Chapter 10: Forecasting with Time Series Data
Chapter 11: Introduction to Data Mining
Chapter 12: Supervised Data Mining: k-Nearest Neighbors and Naive Bayes
Chapter 13: Supervised Data Mining: Decision Trees
Chapter 14: Unsupervised Data Mining
Chapter 15: Spreadsheet Modeling
Chapter 16: Risk Analysis and Simulation
Chapter 17: Optimization: Linear Programming
Chapter 18: More Applications in Optimization
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: Answers to Selected Exercises
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: More Topics in Regression Analysis
Chapter 9: Logistic Regression
Chapter 10: Forecasting with Time Series Data
Chapter 11: Introduction to Data Mining
Chapter 12: Supervised Data Mining: k-Nearest Neighbors and Naive Bayes
Chapter 13: Supervised Data Mining: Decision Trees
Chapter 14: Unsupervised Data Mining
Chapter 15: Spreadsheet Modeling
Chapter 16: Risk Analysis and Simulation
Chapter 17: Optimization: Linear Programming
Chapter 18: More Applications in Optimization
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: Answers to Selected Exercises
Accessibility
Creating accessible products is a priority for McGraw Hill. We make accessibility and adhering to WCAG AA guidelines a part of our day-to-day development efforts and product roadmaps.
For more information, visit our accessibility page, or contact us at accessibility@mheducation.com
Need support? We're here to help - Get real-world support and resources every step of the way.
Top