Data Visualization,
2nd Edition

Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

ISBN-13: 9780357929766
Copyright 2025 | Not Yet Published (2024-05-01)
500 pages | List Price: USD $312.95

Camm/Cochran/Fry/Ohlmann's DATA VISUALIZATION: EXPLORING AND EXPLAINING WITH DATA, 2nd Edition, is designed to introduce best practices in data visualization to undergraduate and graduate students. This is one of the first books on data visualization designed for college courses. The book contains material on effective design, choice of chart type, effective use of color, how to both explore data visually and how to explain concepts and results visually in a compelling way with data. The book explains both the "why" of data visualization and the "how." That is, the book provides lucid explanations of the guiding principles of data visualization through the use of interesting examples.

Purchase Enquiry INSTRUCTOR’S eREVIEW COPY

1. Introduction.
Appendix 1.1 Connecting to Data with Power BI. Appendix 1.2 Connecting to Data with Tableau.
2. Selecting a Chart Type.
Appendix 2.1 Creating and Modifying Charts in Power BI. Appendix 2.2 Creating and Modifying Charts in Tableau.
3. Data Visualization and Design.
Appendix 3.1 Editing Charts in Power BI. Appendix 3.2 Editing Charts in Tableau.
4. Purposeful Use of Color.
Appendix 4.1 Using Color in Power BI. Appendix 4.2 Using Color in Tableau.
5. Visualizing Variability.
Appendix 5.1 Visualizing Variability in Power BI. Appendix 5.2 Visualizing Variability in Tableau.
6. Exploring Data Visually.
Appendix 6.1 Exploring Data in PBI. Appendix 6.2 Exploring Data in Tableau.
7. Explaining Visually to Influence with Data.
8. Data Dashboards.
Appendix 8.1 Dashboards in Power BI. Appendix 8.2 Dashboards in Tableau.
9. Telling the Truth with Data Visualization.
Multi-Chapter Case Problems. Piedmont General Hospital. Bayani Bulb Bases. International Monetary Fund Housing Affordability. Demand for Short Term Rentals. Appendix: Wrangling Data: Preparing Data for Visualization.

  • Jeffrey D. Camm

    Jeffrey D. Camm is the Inmar Presidential Chair and senior associate dean of business analytics programs in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he served on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in many professional journals, including Science, Management Science, Operations Research and the INFORMS Journal on Applied Analytics. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati, and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, Dr. Camm has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016 Dr. Camm received the George E. Kimball Medal for service to the operations research profession, and in 2017 he was named an INFORMS fellow.

  • James J. Cochran

    James J. Cochran is associate dean for research, a professor of applied statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has served as a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 50 papers in the development and application of operations research and statistical methods. He has published in numerous journals, including Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. Dr. Cochran received the 2008 INFORMS prize for the Teaching of Operations Research Practice, the 2010 Mu Sigma Rho Statistical Education Award and the 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005 and was named a fellow of the American Statistical Association in 2011 and a fellow of INFORMS in 2017. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In addition, he received the INFORMS President's Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has also served as an operations research or statistics consultant to numerous companies and not-for-profit organizations.

  • Michael J. Fry

    Michael J. Fry is a professor of operations, business analytics and information systems as well as academic director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, Dr. Fry earned his B.S. from Texas A&M University and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. He has also been named a Lindner Research Fellow. Dr. Fry has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia.He has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IIE Transactions, Critical Care Medicine and Interfaces. His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics and public-policy operations. He has worked with many different organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo & Botanical Garden. He was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati.

  • Jeffrey W. Ohlmann

    Jeffrey W. Ohlmann is associate professor of business analytics and a Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska and M.S. and Ph.D. degrees from the University of Michigan. Dr. Ohlmann has been at the University of Iowa since 2003. His research on the modeling and solution of decision-making problems has produced more than two dozen research papers published in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science and the European Journal of Operational Research. He has collaborated with organizations such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections and three National Football League franchises. Because of the relevance of his work to industry, Dr. Ohlmann received the George B. Dantzig Dissertation Award, and he was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

  • Data visualization: This edition brings chapter appendices showing how to use Microsoft Power BI and Tableau to create data visualizations that adhere to best practices. Recognizing that data has to be transformed, edited and cleaned to be useful, this text introduces an appendix on data wrangling. The appendix describes how to clean and transform the data so that it can then be used to create meaningful and effective data visualizations. Four new multi-chapter cases have been added to this edition, requiring the students to utilize the best practices from a number of chapters in the book.

  • Learning Objectives: Each chapter includes a list of the learning objectives for that chapter. The list provides details of what the student should be able to do and understand once they have completed the chapter.

  • Software: Because of wide-spread use and ease of availability, Microsoft Excel is used to illustrate the best practices and principles. Excel has been thoroughly integrated throughout this textbook. Whenever a new type of chart or table is introduced, detailed step-by-step instructions to create the chart or table in Excel. For those instructors wishing to use Power BI or Tableau, end-of-chapter appendices providing step-by-step instructions for recreating and editing the charts using those packages are also included.

  • Notes and Comments: At the end of relevant sections, this text includes key Notes and Comments to give the student additional insights about the material presented in that section. Additionally, margin notes are used throughout the textbook to provide additional insights and tips related to the specific material being discussed.

  • End-of-Chapter Problems: Each chapter contains at least 15 problems to help the student master the material presented in that chapter. The problems are separated into Conceptual and Application problems. Conceptual problems test the student's understanding of concepts presented in the chapter. Application problems are hands-on, and require the student to construct or edit charts or tables.

  • DATAfiles and CHARTfiles: All data sets used as examples and in end-of-chapter problems are Excel files designated as DATAfiles and are available for download by the student. The names of the DATAfiles are called out in margin notes throughout the textbook. Similarly, Excel files with completed charts are available for download and are designated as CHARTfiles.

  • MindTap, a highly customizable online learning platform, offers an interactive eBook, auto-graded and randomized exercises and problems from the textbook, algorithmic Excel activities, chapter overview and problem walk-through videos and interactive visualizations to strengthen students' understanding of course concepts.

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.

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