1. Início
  2. NOVA Information Management School
  3. Pós-Graduação em Sistemas Estatísticos
  4. Pós-Graduação em Sistemas Estatísticos com Especialização em Estatísticas de Bancos Centrais
  5. Descriptive Data Mining

Descriptive Data Mining

Objetivos

Data Mining uses interdisciplinary techniques, such as statistics, data visualization, database systems, and machine learning to identify original, useful, and understandable patterns in data.
This course will familiarize students with Data Mining applications and Data Mining projects lifecycle. Students will learn techniques for understanding and preparing data before building descriptive models, such as clustering or association rules (e.g., market basket analysis).

Caracterização geral

Código

400070

Créditos

6.0

Professor responsável

Nuno Miguel da Conceição António

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

Familiarity with the main theme of the course is not required. But it is highly recommended that the students have knowledge of Inferential Statistics as well as good skills as a computer user.

Bibliografia

Método de ensino

The course is based on theoretical and practical classes. Several teaching strategies are applied, including slides presentation, step-by-step instructions on how to approach practical examples, and questions and answers. The practical component is oriented towards the exploration of the tools introduced to students (Microsoft Excel and SAS Enterprise Miner) and the development of the project.

Método de avaliação

1 st Season: Exam (60%), Project (40%)
2 nd Season: Exam (60%), Project (40%)

 

Rules:

  • Minimum grade in both the exam and the term project is 8.0 (out of 20)
  • Projects not submitted in Moodle until the deadline will be rejected
     

Conteúdo

LU1. Introduction to Data Mining
LU2. Methodological aspects (KDD, SEMMA, CRISP-DM) LU3. Data visualization
LU4. Data understanding
LU5. Data preparation
LU6. Clustering
LU7. Self-Organizing maps
LU8. RFM model
LU9. Association rules and the Apriori algorithm
LU10. Data similarity and dissimilarity measures