[AISWorld] Announcing the Publication of Business Analytics: Communicating with Numbers, by Jaggia, Kelly, Lertwachara, and Chen, Second Edition

[Leida Chen]

Dear Colleagues,

If you teach Business Analytics, you may be interested in the recently
published second edition of the popular textbook, *Business Analytics:
Communicating with Numbers*, authored by Jaggia, Kelly, Lertwachara, and
Chen, published by McGraw-Hill. The first edition of the text was named
Product of the Year for McGraw-Hill due to extremely positive instructor
feedback and wide adoption. The text was developed from the ground up and
prepares students to understand, manage, and visualize the data, apply the
appropriate analytic techniques and tools using various software and
programming tools, and communicate the findings and their relevance through
engaging storytelling. Using a project-based pedagogical approach, this
text seamlessly threads the topics of data wrangling, descriptive
analytics, predictive analytics or data mining, prescriptive analytics, and
data storytelling into a cohesive whole. Hands-on instructions for Excel,
Analytic Solver (Excel Add-in) and R are included in each chapter. A Python
supplement is also available for instructors who choose to use Python as
the tool for analytics. In addition to detailed analytics cases for
illustrating the concepts and techniques, each chapter comes with over 40
hands-on exercise problems and Writing with Big Data cases. The e-magazine (
https://www.mheducation.com/highered/ideas/i/1444929-jaggia-business-analytics-2e/0?)
provides a detailed look at the content of the text, including two sample
chapters.

If you are interested in reviewing the text or getting access to
McGraw-Hill’s Connect digital course and instructor resources for this
text, please use the following links to find your rep and order a desk
copy: https://www.mheducation.com/highered/product/1264302800.html.

Please also feel free to reach out to McGraw-Hill analytics product team (
Kristen.Salinas at mheducation.com) or the author team (lchen24 at calpoly.edu)
for questions and comments. We welcome your questions and feedback about
the text.

Below is a brief TOC for the text:

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 Naïve Bayes
Chapter 13: Supervised Data Mining: Decision Trees
Chapter 14: Unsupervised Data Mining: Cluster Analysis and Association Rules
Chapter 15: Spreadsheet Modeling
Chapter 16: Risk Analysis and Simulation
Chapter 17: Optimization: Linear Programming
Chapter 18: More Applications in Optimization
Appendixes
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
Online Supplement: Individual Chapter Python support & Getting Starting
with Python

Dr. Leida Chen

Professor and Chair of Management, HR, and IS

Orfalea College of Business

Cal Poly, San Luis Obispo, CA

(805)756-1768

lchen24 at calpoly.edu