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Business Analytics, Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today’s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive and predictive perspectives.
Content is organized into five parts to guide students through the information:
• Part 1: Foundations of Business Analytics. The first two chapters provide the basic foundations needed to understand business analytics and Microsoft Excel, and show students how to manipulate data and develop simple spreadsheet models.
• Part 2: Descriptive Analytics. Chapters 3 through 7 focus on the fundamental tools and methods of data analysis and statistics, focusing on visual representations of data, descriptive statistical measures, probability distributions and data modeling, sampling and estimation, and statistical inference.
• Part 3: Predictive Analytics. Chapters 8 through 12 develop approaches for building and analyzing predictive models, applying regression and forecasting techniques, simulation and risk analysis, and an introduction to data mining.
• Part 4: Prescriptive Analytics. Chapters 13 through 17 explore linear, integer, and nonlinear optimization models and applications including optimization with uncertainty.
• Part 5: Making Decisions. Chapter 18 focuses on philosophies, tools, and techniques of decision analysis.
Contents
PART 1: Foundations of Business Analytics
1. Introduction to Business Analytics
2. Analytics on Spreadsheets
Part 2: Descriptive Analytics
3. Visualizing and Exploring Data
4. Descriptive Statistical Measures
5. Probability Distributions and Data Modeling
6. Sampling and Estimation
7. Statistical Inference
Part 3: Predictive Analytics
8. Trendlines and Regression Analysis
9. Forecasting Techniques
10. Introduction to Data Mining
11. Spreadsheet Modeling and Analysis
12. Monte Carlo Simulation and Risk Analysis
Part 4: Prescriptive Analytics
13. Linear Optimization
14. Applications of Linear Optimization
15. Integer Optimization
16. Decision Analysis
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