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Business Analytics: Descriptive, Predictive, Prescriptive with MindTap

Author(s): Jeffrey D. Camm | James J Cochran | Michael J. Fry | Jeffrey W. Ohlmann

ISBN: 9789360533090

5th Edition

India Release: 2026

₹1375

Binding: Paperback

Pages: 1040

Trim Size: 279 x 216 mm

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Business Analytics, 5th Edition is a comprehensive introduction to the concepts, methods, and applications of business analytics for undergraduate and graduate students. Suitable for learners with or without a background in statistics, the text presents analytical and statistical concepts through practical, real-world business examples. Recognized as one of the pioneering resources in the field, this edition reflects the rapidly evolving analytics landscape with significant updates throughout. The fifth edition features new chapters, emerging concepts, and contemporary analytical tools that help students develop data-driven decision-making skills, solve business problems effectively, and gain a strong foundation for success in today's data-centric business environment.

New Chapter on Data Wrangling covering data access, cleaning, enrichment, validation, and preparation for analysis.
Expanded Data Mining Coverage with deeper treatment of clustering, association rules, text mining, and Principal Component Analysis (PCA).
Separate Chapters for Predictive Analytics focusing on regression models and classification models for greater depth and clarity.
Introduction to Orange (Python-Based Analytics) through new online appendices for machine learning and data visualization.
Updated R Content featuring RStudio-based workflows, scripts, examples, and practice exercises.
Enhanced Descriptive Analytics & Data Visualization including frequency polygons, waterfall charts, choropleth maps, cartograms, and visualization best practices.
Expanded Optimization Models with transportation, assignment, diet, and heuristic optimization using Excel's Evolutionary Solver.
Larger Real-World Data Sets for regression and forecasting applications, reflecting modern business scenarios..
Data & Model Files including Excel, CSV, R scripts, and Orange workflow files.
Extensive Practice Opportunities through chapter exercises, case studies, software applications, and capstone projects.

1. Introduction to Business Analytics
2. Descriptive Statistics
3. Data Visualization
4. Data Wrangling: Data Management and Data Cleaning Strategies
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 Tasks

11. Predictive Data Mining: Classification Tasks

12. Spreadsheet Models

13. Monte Carlo Simulation

14. Linear Optimization Models

15. Integer Linear Optimization Models

16. Nonlinear Optimization Models

17. Decision Analysis

Jeffrey D. Camm

Jeffrey D. Camm is the Inmar Presidential Chair in Analytics and Senior Associate Dean of Faculty in the School of Business at Wake Forest University.

James J. Cochran

James J. Cochran is Professor of Applied Statistics, the Rogers-Spivey Faculty Fellow, and Associate Dean for Faculty and Research at the University of Alabama.

Michael J. Fry

Michael J. Fry is Professor of Operations, Business Analytics, and Information Systems and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati.

Jeffrey W. Ohlmann

Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa.