Many problems in social and laboratory sciences must deal with various sources of uncertainty which in turn impact reproducibility of the conclusions. Uncertainty quantification (UQ) provides methods for determining, assessing, and estimating the effects of uncertainty. This course will focus on statistical approaches to estimating uncertainty in different applications including those associated with predictive analysis and learning algorithms. We will consider various uncertainty measures and data-based estimation techniques for them, using resampling methods and computer simulation based methods. Prerequisites: Math 494 and Math 5061 or by permission of the instructor.