Full course description
This 30h course leads to a Visual Analytics Certificate by the School of Informatics, Computing, and Engineering (SICE) at Indiana University. Delivered entirely online, all coursework can be completed asynchronously to fit busy schedules.
Duration: 6 weeks x 5 hours = 30 hours (3 CEUs)
Dates: January 14, 2019–February 22, 2019
Fee: $950 per student*
Victor H. Yngve Distinguished Professor of Engineering and Information Science at the School of Informatics, Computing, and Engineering. Founding Director of the Cyberinfrastructure for Network Science Center (https://cns.iu.edu) at Indiana University.
Develops data analysis and visualization techniques for information access, understanding, and management.
Cyberinfrastructures development for large-scale scientific collaboration and computation.
Michael GindaAssistant Instructor
Data analyst and research assistant with the Cyberinfrastructure for Network Science Center, SICE. He holds a Master’s degree in Library Science from Indiana University.
Research focus on knowledge representation and organization, metadata, and information networks.
Lead instructional designer.
Andreas BueckleAssistant Instructor
PhD student in Information Science at SICE performing research on information visualization.
Research focus on information visualization, specifically for virtual and augmented reality.
Videography and photography.
At course completion, students will have:
- A theoretical foundation of how to design effective analysis workflows and data visualizations;
- Hands-on skills in the application of visual analytics in real-world case studies;
- Expertise on how new methods and tools can create value across the product life cycle;
- An understanding of how to define, measure, and improve data visualization literacy within an institution; and
- Knowledge of research challenges and application areas that drive data-driven decision making across industries.
Hands on projects ask students to apply new knowledge to identify user needs and priorities; select the best data, algorithms, and workflows for temporal, geospatial, topical, and network case studies; communicate actionable insights using standard terminology; and deliver high-quality results on time and on budget.
Monitor S&T Developments
Optimize Traffic Flows
Grading and Completion Criteria
Students must achieve a score of 70% or higher to pass the course. Final grade is based on:
- Pre- and Post-Assessment (10%)
- Theory and Concept Questions (30%)
- Hands-on Case Studies (40%)
- Discussions/Peer Review (20%)
*Please contact Elizabeth Record for institution-specific bulk registrations.