1. descriptive models

2. predictive models

3. causality

At end of this course each student shall be able to:

 • Recognize the role and importance of extracting knowledge from data in the more general context of building analytics systems in Data Science environments;

 • Identify the main methodologies and knowledge extraction tools in descriptive and predictive models, as well as in cause-and-effect relationships;

 • Apply knowledge extraction techniques in an experimental context.

1. Descriptive Models

1.1. Association Rules

1.2. Clustering

2.  Predictive Models

2.1. Classification

2.2. Regression

3. Causality

3.1. Causal discovery

3.2. Causal inference

• Introduction to Data Mining de Pang-Ning Tan, Michael Steinbach e Vipin Kumar, http://www-users.cs.umn.edu/%7Ekumar/dmbook

• Extração de Conhecimento de Dados/ Data Mining (3ª Edição) de João Gama, Ana Carolina Lorena, Katti Faceli, Márcia Oliveira e André Ponce de Leon Carvalho editor: Edições Sílabo ‧ isbn: 9789726189145

• The Effect, de Nick Huntington-Klein https://theeffectbook.net/index.html

E-learning

Students\' evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.

The students are supposed to be proficient in Portuguese language