Visual Analytics for Sensemaking
Start Date: October 16, 2017
End Date: December 4, 2017
Visual Analytics for Sensemaking is one of four non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. 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.
This course can be taken individually, or as part of the four courses required to receive the CPDA certificate of completion. It is strongly recommended that participants who take the Foundations of Statistics first, followed by Data Mining. Machine Learning or Visualization Analytics and Sensemaking can follow in any order.
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, R, D3) to create data-driven visualizations
- Effectively critique others’ work and receive a critique.
It is strongly recommended that participants have completed the Foundation of Statistics before taking this course.
Students will be required to learn the R software package prior to starting the course (www.r-project.org).
Free training in R software that will prepare you for this course can be found online at:
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.