Introduction to Data Science
- apply estimation procedures
- derive properties, such as bias, consistency, sufficiency, efficiency, of estimation procedures;
- show the proof for the Cramer-Rao lower bound and the Asymptotic efficiency of MLE;
- derive and apply maximum likelihood, method-of-moments and Bayesian estimation;
- apply Bayesian computational approaches;
- apply hypothesis testing and derive its properties;
- apply estimation and testing principles to linear regression.
In inductive practice we are interested to learn about the state of the world given some event, i.e., the data. In this course we will learn about ``estimation'' procedures, in particular maximum likelihood and the method of moments, and some of their theoretical properties. We also learn about hypothesis testing. Then we apply both estimation and testing to a practical setting: linear regression analysis. The course will be offered online as well to also allow double-degree Master students to enroll.
Combination of lectures and tutorials
Final written exam
- Statistical Inference, Casella and Berger, Duxbury, 2th edition, 2009.
- Master of Science in Computational Science, Foundation course, 1° anno