EGRMGMT585

In this course, students will learn the fundamentals of data science, including core technical vocabulary and mathematical concepts. This will include topics such as (i) probability through Bayesian techniques; (ii) binary classification; (iii) linear regression for forecasting; (iv) Information measures used in data science, including mutual information, relative entropy (KL divergence), and log loss (cross entropy), (v) Experimental design; and (vi) the roles of training and test data, using Hoeffding's inequality to forecast error rates. Students will apply the above concepts to real-world data, while developing their own models for probabilistic forecasting.

No Special Consent Required