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Introduction to Data Science

Description

COURSE OBJECTIVES

  • 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.

 

COURSE DESCRIPTION

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.

 

LEARNING METHODS

Combination of lectures and tutorials

 

EXAMINATION INFORMATION
Final written exam

 

REFERENCES

  • Statistical Inference, Casella and Berger, Duxbury, 2th edition, 2009.

People

 

Wit E. C.

Course director

Ceoldo G.

Assistant

Additional information

Semester
Fall
Academic year
2021-2022
ECTS
6
Language
English
Education
Master of Science in Computational Science, Foundation course, 1st year