Business Analytics

Code:

1OP12

Acronym:

BA

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Institution Responsible:	Faculty of Economics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
LECO	35	Bologna Syllabus since 2012	3	-	3	-	81
LGES	20	Bologna Syllabus since 2012	3	-	3	-	81

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
Facts and Dimensions. hierarchies
Data visualization.
Knowledge: Representation of knowledge.
Classification, and cluster analysis.

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.

It is recommended that students use their personal computer in class.

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