Business Analytics
Keywords |
Classification |
Keyword |
OFICIAL |
Computer Science |
Instance: 2021/2022 - 1S ![Requerida a integração com o Moodle Ícone do Moodle](/fep/pt/imagens/MoodleIcon)
Cycles of Study/Courses
Teaching language
English
Objectives
After completing the course, the student must:
To know:
1)Structuring information in multidimensional databases
2) the various types of data mining tasks (Data Mining);
3) to know the main methods / algorithms for each type of task;
and be able to:
a) apply these methods to a new data analysis problem;
b) evaluate the results and understand the methods studied.
Learning outcomes and competences
Structuring information in a multidimensional database
Knowledge how to formulate a problem as a problem of knowledge extraction.
Ability to apply methods / algorithms to a new data analysis problem, and to evaluate the results and understand the operation of the studied methods.
Working method
Presencial
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
Basic knowledge of data bases
Program
Multidimensional databases. PowerBI
Knowledge: Knowledge representation.
Data mining tools. - Data mining project methodologies (CRISP-DM)
Classification,
Text Mining and Web Mining
Classification, cluster analysis, change detection.
Analysis of social networks.
Mandatory literature
Gama João; Extração de Conhecimento de Dados Data Mining, Silabo, 2017
Teaching methods and learning activities
The course combines formal lectures with computer lab sessions. The former are dedicated to the presentation of the methods and tools for knowledge mining, whereas the latter open the way for hands-on in-class work allowing students to work directly with the data, implement the models and interpret the results, thereby assuring their autonomy in future work
Evaluation Type
Distributed evaluation with final exam
Assessment Components
Designation |
Weight (%) |
Apresentação/discussão de um trabalho científico |
30,00 |
Trabalho prático ou de projeto |
30,00 |
Exame |
40,00 |
Total: |
100,00 |
Amount of time allocated to each course unit
Designation |
Time (hours) |
Estudo autónomo |
20,00 |
Frequência das aulas |
39,00 |
Elaboração de projeto |
10,00 |
Trabalho escrito |
12,00 |
Total: |
81,00 |
Eligibility for exams
Approval in the two assessments
Calculation formula of final grade
0.3*HW1 + 0.3*HW2+ 0.4*Exam