3.00 Credits
This course covers the role of the scientific method in applied data science with a focus on topics such as incomplete data, temporal cadence, systematic biases, rejection of outlier data, constraints on the mathematical modeling systems arising from underlying scientific considerations, experimental design, signal to noise, time-dependent modeling, ensemble modeling, statistical image analysis, and scientific numerical methods. NOTE: Please refer to the appropriate academic catalog for additional course information concerning prerequisites, co-requisites and course restrictions.