Susan is a global product marketing manager at SAS, focused on helping customers explore the possibilities of SAS Viya. With a Ph.D. in Human Factors and Ergonomics, she brings a unique perspective on how people learn, work and interact with technology. Her background spans user-centered design, product management, customer insights and operational risk, giving her a practical view of how analytics can improve experiences and drive better outcomes.
Let's talk about using DLPy to model employee retention through a survival analysis model. Survival analysis is used to model time-to-event. Examples of time-to-event include the time until an employee leaves a company, the time until a disease goes into remission, or the time until a mechanical part fails. The
Computer vision can augment radiologists and make the image interpretation process cheaper, faster and more accurate. The ultimate goal is to achieve a better patient outcome facilitated by the use of computer vision.
With NHANES data from the CDC, we can use SAS to research the impact of endocrine-disrupting chemicals. Special sampling techniques are needed when using these data.
