Description

Theory/Practice
2

Laboratory
2

Instructors

Sandra Ramos

Contents

SY1. Brief introduction to the Python language;
SY2. Types of variables; Processing and selection of data; Data visualization; Data summary; Detection and correction of outliers; dimensionality reduction;
SY3. Statistical inference: brief review;
SY4. Regression: linear; logistics and Poisson. Model comparison. Selection of covariates (by stepwise); Extrapolation;
SY5. Linear and quadratic discriminant analysis.

Learning Outcomes

It is intended that students gain fundamental knowledge that underlies the resolution of problems of statistical inference, under both the classical paradigm and the Bayesian paradigm. At the end of the term, students are able to:

LO1. Know the different types of data and data collection processes;
LO2. Use data representation and visualization processes;
LO3. Work with multivariate data and use statistical inference techniques;
LO4. Know different regression techniques and solve regression problems;
LO5. Know different discriminant analysis techniques and solve problems that split objects into two or more classes;
LO6. Know different statistical decision techniques and solve problems decision making using probabilities and costs associated with classification;
LO7. Develop technical reports that are easy to update, maintain, replicate and publish.