Métodos Descritivos de Data Mining
Objetivos
The curricular unit of descriptive methods of data mining has a primary goal of allowing students to gather skills in extracting information and knowledge from large databases. The success of information systems over the last decades promoted huge databases. These databases have the potential to foster development and significantly increase the wealth of our society, allowing problem resolution. We can say that these data repositories can provide precious resources for improving the quality of decision-making.
Thus, this course addresses the topics and issues typically associated with designations of "Data Mining" or "Knowledge Discovery." This field has been developing rapidly and has become central to various activities. Its contribution extends the scientific marketing research, constituting a significant body of knowledge and is still expanding.
Caracterização geral
Código
200291
Créditos
7.5
Professor responsável
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
No requirements are needed
Bibliografia
Método de ensino
The curricular unit is based on Team-based learning (TBL). TBL is a pedagogical approach in which multiple choice questions are used simultaneously as summative assessment and as triggers for deep learning. Continuous summative assessment runs through a whole course from beginning to the very end.
In TBL students prepare a new topic before it is taught in the classroom. The preparatory stage can include anything from reading textbooks to online materials and exercises.
Each class starts with an individual summative quiz, followed by the same quiz done as a small group examination assignment. Group discussion is prompted by the examination assignment.
The practical component includes exercises to consolidate the theoretical concepts covered in the theoretical classes.
Método de avaliação
Regular period (1st examination period)
|
Eval. element |
Number |
% |
|
Individual Readiness Assurance Test (iRAT) |
6 |
2.5 |
|
Team Readiness Assurance Test (tRAT) |
6 |
2.5 |
|
Handout 1 |
1 |
5 |
|
Handout 2 |
1 |
5 |
|
Final project |
1 |
40 |
|
Exam |
1 |
20 |
Resit period (2nd examination period)
- Exam (65%)
- Final project (35%)
Minimum grade of 8.0 (in 20) for the exam
Conteúdo
LU1. Introduction to Data Mining
LU2. Data visualization
LU3. Data pre-processing
LU4. Introduction to cluster analysis
LU5. Cluster analysis using SOMs
LU6. Cluster assessment
LU7. Introduction to Association rules