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<div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Dear Colleagues,</div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">If you teach Business or Data Analytics, you may be interested in the recently published new edition of the popular textbook, <i>Business Analytics: Communicating with Numbers</i>,
authored by Jaggia, Kelly, Lertwachara, and Chen, published by
McGraw-Hill. The text was named Product of the Year for McGraw-Hill and
widely adopted by business/data analytics courses at both the
undergraduate and graduate levels.</div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Using
a project-based pedagogical approach, this text seamlessly threads the
topics of data wrangling, descriptive analytics, predictive analytics,
data mining and machine learning, prescriptive analytics, and data
storytelling into a cohesive whole. Hands-on instructions for Excel,
Analytic Solver (Excel Add-in), R, and Python are included in each
chapter. In addition to detailed analytics cases for illustrating the
concepts and techniques, each chapter comes with over 50 hands-on
exercise problems with datasets and Writing with Big Data cases. Please
see the product flyer (<u><span style="display:inline-block" class="gmail-x__Entity gmail-x__EType_OWALink gmail-x__EId_OWALink gmail-x__EReadonly_1"><span><a class="gmail-x_OWAAutoLink gmail-x_none" id="gmail-OWAdb1218e0-1d01-cfc0-9a74-43749557bc32" rel="noopener noreferrer" target="_blank" href="https://cpslo-my.sharepoint.com/:b:/g/personal/lchen24_calpoly_edu/Ecqpn71C32ZOtU-ZytpZK9cB9BRFp2Xj3YUg-d12m3Oo7g?e=RWcs4h" title="https://cpslo-my.sharepoint.com/:b:/g/personal/lchen24_calpoly_edu/Ecqpn71C32ZOtU-ZytpZK9cB9BRFp2Xj3YUg-d12m3Oo7g?e=RWcs4h">https://cpslo-my.sharepoint.com/:b:/g/personal/lchen24_calpoly_edu/Ecqpn71C32ZOtU-ZytpZK9cB9BRFp2Xj3YUg-d12m3Oo7g?e=RWcs4h</a></span></span></u>) for an overview of the features of the text and the Preface (<u><span style="display:inline-block" class="gmail-x__Entity gmail-x__EType_OWALink gmail-x__EId_OWALink_1 gmail-x__EReadonly_1"><span><a class="gmail-x_OWAAutoLink gmail-x_none" id="gmail-OWAb5fe0f33-69d7-8865-3ab9-223b297eef2d" rel="noopener noreferrer" target="_blank" href="https://cpslo-my.sharepoint.com/:b:/g/personal/lchen24_calpoly_edu/ESgAPa0W04FCkbzIemnvyjcBF1lvfp6ltpTl2kDzj5uwqA?e=OJjCjN" title="https://cpslo-my.sharepoint.com/:b:/g/personal/lchen24_calpoly_edu/ESgAPa0W04FCkbzIemnvyjcBF1lvfp6ltpTl2kDzj5uwqA?e=OJjCjN">https://cpslo-my.sharepoint.com/:b:/g/personal/lchen24_calpoly_edu/ESgAPa0W04FCkbzIemnvyjcBF1lvfp6ltpTl2kDzj5uwqA?e=OJjCjN</a></span></span></u>) for a detailed Table of Contents.</div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt"><span style="color:rgb(0,0,0)">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 contact your McGraw-Hill rep or use the following links to
request a desk copy: </span><span style="color:blue"><u><a style="color:blue" class="gmail-x_OWAAutoLink" id="gmail-OWA68ace5f1-e9c3-8076-76ff-3c9ec48ced39" rel="noopener noreferrer" target="_blank" href="https://www.mheducation.com/highered/product/Business-Analytics-Jaggia.html?viewOption=instructor&release=2025+Release" title="https://www.mheducation.com/highered/product/Business-Analytics-Jaggia.html?viewOption=instructor&release=2025+Release">https://www.mheducation.com/highered/product/Business-Analytics-Jaggia.html?viewOption=instructor&release=2025+Release</a></u></span><span style="color:rgb(0,0,0)">. </span></div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt"><span style="color:rgb(0,0,0)">Please also feel free to reach out to McGraw-Hill analytics product team (</span><span style="color:blue"><u><a style="color:blue" class="gmail-x_OWAAutoLink" id="gmail-OWA2829c9d0-7744-0512-2abd-a9ac2ba155bb" href="mailto:Kristen.Salinas@mheducation.com" title="mailto:Kristen.Salinas@mheducation.com">Kristen.Salinas@mheducation.com</a></u></span><span style="color:rgb(0,0,0)">) or the author team (<a href="mailto:lchen24@calpoly.edu">lchen24@calpoly.edu</a>) for questions and comments. We welcome your questions and feedback about the text.</span></div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Below is a brief TOC for the text:</div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)" class="gmail-x_elementToProof">Chapter 1: Introduction to Business Analytics<br>Chapter 2: Data Management and Wrangling<br>Chapter 3: Summary Measures<br>Chapter 4: Data Visualization<br>Chapter 5: Probability and Probability Distributions<br>Chapter 6: Statistical Inference<br>Chapter 7: Regression Analysis<br>Chapter 8: More Topics in Regression Analysis<br>Chapter 9: Logistic Regression<br>Chapter 10: Forecasting with Time Series Data<br>Chapter 11: Introduction to Data Mining<br>Chapter 12: Supervised Data Mining: k-Nearest Neighbors and Naïve Bayes<br>Chapter 13: Supervised Data Mining: Decision Trees<br>Chapter 14: Unsupervised Data Mining: Cluster Analysis, Association Rules, and Text Analytics<br>Chapter 15: Spreadsheet Modeling<br>Chapter 16: Risk Analysis and Simulation<br>Chapter 17: Optimization: Linear Programming<br>Chapter 18: More Applications in Optimization<br>Appendixes<br>Appendix A: Big Data Sets: Variable Description and Data Dictionary<br>Appendix B: Getting Started with Excel and Excel Add-Ins<br>Appendix C: Getting Started with R</div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)" class="gmail-x_elementToProof">Appendix D: Getting Started with Python<br>Appendix E: Answers to Selected Exercises</div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)" class="gmail-x_elementToProof"><br></div><div style="line-height:1.284;margin:0in 0in 8pt;font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)" class="gmail-x_elementToProof">
<div style="text-indent:0px;font-family:Helvetica,serif,EmojiFont;font-size:12px">Dr. Leida Chen</div><div style="text-indent:0px;font-family:Helvetica,serif,EmojiFont;font-size:12px">Professor and Chair of Management, HR, and IS</div>
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