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Overview
Present the full range of analytics -- from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann's market-leading BUSINESS ANALYTICS, CENGAGE INTERNATIONAL EDITION 5E. Clear, step-by-step instructions teach students how to use Excel, Tableau, R or the Python-based Orange data mining software to solve more advanced analytics concepts. As instructor, you choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time while providing students with critical practice. Updates throughout this edition cover topics beyond the traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, MindTap and WebAssign customizable online learning platforms offer an interactive eBook, auto-graded exercises, algorithmic practice problems and Exploring Analytics visualizations to strengthen students' understanding.
- NEW CHAPTER ON DATA WRANGLING (CH. 4) PROVIDES CRITICAL INSIGHTS INTO THIS IMPORTANT TOPIC. New content covers issues such as how to access and structure data for exploration, how to clean and enrich data to facilitate analysis and how to validate data.
- COVERAGE OF PREDICTIVE DATA MINING TECHNIQUES NOW INCLUDES TWO SEPARATE CHAPTERS (CHS. 10, 11) One chapter now focuses on predicting quantitative outcomes with k-nearest neighbors regression, regression trees and neural network regression. A second chapter discusses predicting binary categorical outcomes with k-nearest neighbors classification, classification trees, support vector classifiers and neural network classifiers.
- NEW ONLINE APPENDIXES INTRODUCE THE SOFTWARE PACKAGE ORANGE FOR DESCRIPTIVE AND PREDICTIVE DATA-MINING MODELS. Students learn this easy-to-use, yet powerful, workflow-based approach to building analytics models using Orange, the open-source machine learning and data visualization software package built using Python. This coverage of Orange and Python complements the book's existing coverage of R for solving descriptive and predictive analytics models. New practice problems and solutions in the R and Orange appendices strengthen students' problem-solving skills.
- EXPANDED COVERAGE OF DESCRIPTIVE ANALYTICS METHODS, INCLUDING DATA VISUALIZATION, DISCUSSES NEW TOPICS. Coverage of histograms (Ch. 2) now includes a discussion of frequency polygons as a way of exploring data. Chapter 3’s coverage of data visualization now offers a more comprehensive discussion of best practices in data visualization, including the use of preattentive attributes and the data-ink ratio to create effective tables and charts.
- REORDERED CHAPTER CONTENT AND NEW COVERAGE OF CHARTS AND MAPS FURTHER STRENGTHEN THE BOOK'S COMPREHENSIVE APPROACH. The authors have carefully rearranged key chapter material for clarity. This edition also includes new coverage of table lens, waterfall charts, stock charts, choropleth maps and cartograms.
- LARGER, MORE REALISTIC DATA SETS BETTER PREPARE STUDENTS FOR SUCCESS ON THE JOB. The authors have increased the size of many data sets in Chapter 8 on linear regression and Chapter 9 on time series analysis and forecasting. This data sets now better represent real data sets students will encounter in practice.
- NEW LEARNING OBJECTIVES IN EACH CHAPTER DIRECT STUDENT ATTENTION TO KEY CONCEPTS. These new Learning Objectives appear at the beginning of each chapter and preview the important concepts that are covered in that chapter. Each problem is now identified by Learning Objectives so you can easily determine which problems to assign for additional practice and review.
- COMPLETELY INTEGRATED COVERAGE OF EXCEL DEMONSTRATES THE LATEST METHODS FOR SOLVING PRACTICAL PROBLEMS. Clear, step-by-step instructions teach students to use Excel as a tool for applying concepts in the book. The authors also include by-hand calculations to highlight specific analytical insights, when appropriate. Fully updated Excel instructions correspond to the latest versions of Excel.
- STEP-BY-STEP INSTRUCTIONS EXPLAIN IMPORTANT ANALYTICAL STEPS. Helpful instructions show students how to use a variety of leading software programs to perform the analyses discussed in the text.
- STEP-BY-STEP INSTRUCTIONS EXPLAIN IMPORTANT ANALYTICAL STEPS. Helpful instructions show students how to use a variety of leading software programs to perform the analyses discussed in the text.
- PRACTICAL, RELEVANT PROBLEMS HELP STUDENTS MASTER CONCEPTS AND HANDS-ON SKILLS. Applications drawn from all functional business areas, including finance, marketing and operations, provide important practice at various levels of difficulty. Time-saving data sets are available for most exercises and cases.
- ANALYTICS IN ACTION EFFECTIVELY DEMONSTRATE THE IMPORTANCE OF CONCEPTS IN BUSINESS TODAY. Each chapter contains an Analytics in Action feature that presents interesting examples of how professionals use business analytics in actual practice. These timely, engaging examples are drawn from organizations in a variety of areas, including healthcare, finance, manufacturing and marketing.
- ONLINE DATA FILES AND MODEL FILES SAVE TIME. All data sets used as examples and used within student exercises are provided online for convenient student download. DATAfiles are files that contain data that corresponds to examples and problems given in the text. MODELfiles contain additional modeling features that highlight the extensive use of Excel formulas or the use of other software such as R and Orange.
- ONLINE DATA FILES AND MODEL FILES SAVE TIME. All data sets used as examples and used within student exercises are provided online for convenient student download. DATAfiles are files that contain data that corresponds to examples and problems given in the text. MODELfiles contain additional modeling features that highlight the extensive use of Excel formulas or the use of other software such as R and Orange.
2. Descriptive Statistics.
3. Data Visualization.
4. Data Wrangling.
5. Probability: An Introduction to Modeling Uncertainty.
6. Descriptive Data Mining.
7. Statistical Inference.
8. Linear Regression.
9. Time Series Analysis and Forecasting.
10. Predictive Data Mining: Regression.
11. Predictive Data Mining: Classification.
12. Spreadsheet Modeling.
13. Monte Carlo Simulation.
14. Linear Optimization Models.
15. Integer Linear Optimization Models.
16. Nonlinear Optimization Models.
17. Decision Analysis.
Appendix A: Basics of Excel.
Appendix B: Database Basics with Microsoft Access.
Appendix C: Solutions to Even-Numbered Questions (online).
Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.
FOR STUDENTS
International MindTap Instant Access for Camm/Cochran/Fry/Ohlmann's Business Analytics
ISBN: 9780357990322
International MindTap Instant Access for Camm/Cochran/Fry/Ohlmann's Business Analytics, 5th Edition is the digital learning solution that powers students from memorization to mastery. It gives you complete control of your course--to provide engaging content, to challenge every individual, and to build their confidence. Empower students to accelerate their progress with MindTap. MindTap: Powered by You.
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