Visual Analytics for Sensemaking
Start Date: January 6th, 2020
End Date: February 24th, 2020
Visual Analytics for Sensemaking is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion.
Visual Analytics for Sensemaking is the fourth course in the sequence of the CPDA program. After learning how to analyze data statistically, the data mining methodology, advanced analytics or machine learning, students learn the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with the use of data visualization software. While this course is the fourth in the sequence, it can be take at any time if necessary because there are no prerequisites.
The course is taught by faculty from the College of Engineering. The course is delivered in 100% distance learning format and includes instructional material equivalent to a one semester credit hour class.
Visual Analytics for Sensemaking is an introduction to creating visual representations that depict the meaning of big data for businesses and individuals to better understand and use for decision making and planning. This course covers such topics as replanning, deciding, detecting events, and coordination as well as concepts and techniques for the design of visualizations to support sensemaking in a variety of settings. Students have the opportunity to design and build visualizations, using the concepts learned in class.
4 CEUs are granted upon successful completion of the course.
You Will Learn to:
- Perform a Work Domain Analysis to reveal informative data relationships for a specific context in a given setting.
- Apply the basic techniques of Representation Design in order to organize the visual field in an informative way.
- Use low-fidelity tools (i.e., pen and paper) to iteratively experiment with standard and compound visual forms to create informative representations.
- Use high-fidelity tools (e.g., Tableau) to create data-driven visualizations
- Effectively critique others’ work and receive a critique.
Click Here to learn more about how this course is delivered 100% online!
Expected Time Commitment to Complete this Course
Each course is equivalent to a one semester credit hour class. Therefore each class consists of approximately 40 hours of class time that includes 12-13 hours of recorded faculty lectures and 23-24 hours of additional course work. Each course is seven weeks in length, so each week there is 5.7 hours of combined class time (40 hrs / 7 weeks). The average student should allow a 2:1 study-to-class-time ratio to complete the course. This means you should plan to study two hours for each one hour of class time. This equates to 11-12 hours each week to complete all course work. (5.7 hrs X 2 = 11-12 hrs). Based on a person's own personal strengths and experience, you should increase or decrease the ratio.
Cancellations and Refunds
A full refund minus a $50 administrative fee will be made if cancellation is received three weeks prior to the start of the course. No refunds within three weeks of the course start date.
Course Offering Dates
Each course offering in this program is faculty lead therefore it operates with a specific start date and end date. Students must complete each course during the specific time frame. Access to the online course and materials is removed when the course ends.