Information Visualization MOOC 2014


This course provides an overview about the state of the art in information visualization. It teaches the process of producing effective visualizations that take the needs of users into account.

This year, the course can be taken for three Indiana University credits as part of the Online Data Science Program just announced by the School of Informatics and Computing. Students interested in applying to the program can find more information here.

Among other topics, the course covers:

  • Data analysis algorithms that enable extraction of patterns and trends in data
  • Major temporal, geospatial, topical, and network visualization techniques
  • Discussions of systems that drive research and development.
Just like last year, students will have the opportunity to collaborate on real-world projects for a variety of clients. Click here to see the 2014 list of clients and projects. You can also see the detailed results of the 2013 client projects from the Visual Insights book here.

Everyone who registers gains free access to the Scholarly Database (26 million paper, patent, and grant records) and the Sci2 Tool (100+ algorithms and tools).

Please watch the introduction video to learn more.

This site is only for the 2014 IVMOOC.
For those wanting to register for the 2015 IVMOOC, please bear with us as we apply the finishing touches. We will announce on Facebook and Twitter when registration is open.
Thanks for your patience.


Week 1 – Jan. 28, 2014: Visualization Framework & Workflow Design
Week 2 – Feb. 4, 2014: “When": Temporal Data
Week 3 – Feb. 11, 2014: “Where": Geospatial Data
Week 4 – Feb. 18, 2014: “What": Topical Data
Mid-Term: to be taken by Feb. 24 2014 at 5pm EST
Week 5 – Feb. 25, 2014: “With Whom": Trees
Week 6 – Mar. 4, 2014: “With Whom": Networks
Week 7 – Mar. 11, 2014: Dynamic Visualizations & Deployment
Final Exam: to be taken by Mar. 17 2014 at 5pm EST
Week 8 – Mar. 18, 2014: Picking a Client
Week 9 – Mar. 25, 2014: Project Ideas
Week 10 – Apr. 1, 2014: 1st Project Draft
Week 11 – Apr. 8, 2014: 1st Project Draft
Week 12 – Apr. 15, 2014: Peer Feedback
Week 13 – Apr. 22, 2014: 2nd Project Draft
Week 14 – Apr. 28, 2014: Final Project Due / Client Wrap-Up
Week 15 – Presentations

Scott Weingart Scott Weingart

Scott B. Weingart is a Ph.D. student studying information science and history of science at Indiana University. He is an NSF Graduate Research Fellow, a Paul Fortier Prize Winner for the Digital Humanities, and the author of the scottbot irregular, a blog covering computational methods and tools for humanists. Scott also aids in the development of software for data analysis and modeling at the Cyberinfrastructure for Network Science Center at Indiana University.

Michael Ginda Michael Ginda
Assistant Instructor

Michael Ginda is a Masters student at the School of Information and Library Science at Indiana University, specializing in metadata, knowledge representation, and information networks. He is also a graduate and research assistant at the Cyberinfrastructure for Network Science Center, working on Sci2 documentation and instruction materials.

Scott Emmons Scott Emmons
Student Liaison

Scott Emmons is a student at Bloomington High School North and research intern at the Cyberinfrastructure for Network Science Center. Scott plans to be a professor when he is older. His research interests lie at the intersection of business and computer science, and he is a co-founder of the business consulting firm Sparq Creative Solutions. To learn more about Scott and his work, visit

Dr. Katy Borner Katy Börner
Previous Instructor

Katy Börner is the Victor H. Yngve Professor of Information Science at the School of Informatics and Computing, Adjunct Professor at the School of Informatics and Computing, Adjunct Professor at the Department of Statistics in the College of Arts and Sciences at Indiana University where she directs the Cyberinfrastructure for Network Science Center. Her research focuses on the development of data analysis and visualization techniques for information access, understanding, and management. Watch her TEDx talk here.

Suggested Readings

New for 2014, Visual Insights: A Practical Guide to Making Sense of Data was created as a companion textbook to the course. It offers a gentle introduction to the design of insightful visualizations, seamlessly blending theory and practice to give readers both the theoretical foundation and the practical skills to render data into insights. Each chapter has a hands-on section that demonstrates how plug-and-play macroscope tools can be used to run advanced data mining and visualization algorithms. The final two chapters present exemplary case studies and discuss future developments.

Click here to learn more about the book and see a preview. It is available for pre-order now from MIT Press, Amazon, and Barnes and Noble. Order yours today!

Atlas of Science by Katy Börner, based on the popular exhibit, "Places & Spaces: Mapping Science," describes and displays successful mapping techniques. The heart of the book is a visual feast: Claudius Ptolemy's Cosmographia World Map from 1482; a guide to a PhD thesis that resembles a subway map; "the structure of science" as revealed in a map of citation relationships in papers published in 2002; a visual periodic table; a history flow visualization of the Wikipedia article on abortion; a globe showing the worldwide distribution of patents; a forecast of earthquake risk; hands-on science maps for kids; and many more. Each entry includes the story behind the map and biographies of its makers.

Sci2 Tutorial by Scott Weingart, Ted Polley et al. The Science of Science (Sci2) Tool is a modular toolset specifically designed for the study of science. It supports the temporal, geospatial, topical, and network analysis and visualization of datasets at the micro (individual), meso (local), and macro (global) levels. Users of the tool can: access science datasets online or load their own; perform different types of analysis with the most effective algorithms available; use different visualizations to interactively explore and understand specific datasets; share datasets and algorithms across scientific boundaries.


Final grade: 30% Midterm, 40% Final, 30% client project. Participants that receive more than 80% of all available points will receive a personalized, green letter of accomplishment and digital badge (shown below). Those who do not participate in the project but earn more than 80% of all points on exams will receive an orange badge and letter of accomplishment.

Will I get actual credit for taking the course?
The 2014 course can be taken for three Indiana University credits as part of the Online Data Science Program just announced by the School of Informatics and Computing. Students seeking enrollment information should contact Rhonda Spencer at 812-855-2018 or

How much does it cost to take the course?
The course is free, except for those taking the course for credit through Indiana University's Online Data Science Program (click here for details). All of the software and services required for the course are free. Throughout the entirety of this course we will use open-source software and/or freely available services to complete the work required to obtain a letter of accomplishment and badge.

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Acknowledgments: We would like to thank Miguel Lara for instructional design support, Samuel Mills for designing the web pages, Robert P. Light, Thomas Smith, and Adam H. Simpson for extending the GCB platform, and Mike Widner and Mike T. Gallant for adding the Forum. Support comes from CNS, CITL, SLIS, SOIC, the Trustees of Indiana University, and Google.