Information Systems

Code:

2MEAE06

Acronym:

SI

Keywords
Classification	Keyword
OFICIAL	Management Studies

Instance: 2025/2026 - 1S

Active?	Yes
Web Page:	http://moodle.up.pt/course/view.php?id=1647
Responsible unit:	Management
Course/CS Responsible:	Master in Economics and Business Administration

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
EAE	57	Official Syllabus after 2021-2022	1	-	3	21	81

Teaching language

Portuguese

Objectives

The goal of this course is to provide students with a fundamental knowledge on information systems, including  the components of technology, design and analysis of information, in order to make them capable of using them as a tool of promotion of the corporation strategy and objectives.

Learning outcomes and competences

It is intended that students acquire the skills to:

1. Understand the fundamentals of Information Systems
2. Use the main techniques of business intelligence using data from the business context;
3. Follow the CRSP-DM methodologies and understand the principles of Data Mining

Working method

Presencial

Program

1. 1. Information Systems 




1. 1.1. Introduction; Types of IS and applications;


2. 1.2. Datawarehouse and Business Intelligence


3. 1.3. ETL


4. 1.4. Modelos dimensionais: Star Schemas Versus OLAP Cubes




2. 2. Analytics




1. 2.1. Introduction to Power BI


2. 2.2 Dashboards and Exploratory Data Analysis 




3. 2.3. Big Data Analytics:




1. Clusters analysis;


2. Decision trees with Python.

 

Mandatory literature

Ralph Kimball; The data warehouse toolkit. ISBN: 978-1-118-53080-1
Kenneth C. Laudon and Jane P. Laudon; Management Information Systems: Managing the Digital Firm, Prentice Hall, 2013

Complementary Bibliography

Pawel Cichosz; Data Mining Algorithms: Explained Using R, Wiley
Vaisman and Zimányi; Data Warehouse Systems, Springer. ISBN: 978-3642546549
Foster Provost and Tom Fawcett; Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Pedro Campos, João Mendes Moreira, Sara Santos; Web analytics, E-Agro Markets, 2020 (http://e-agroinnovation.agrocluster.com/uploads/AGROCLUSTER_Ebooks%20-%20Web%20Analytics.pdf)

Teaching methods and learning activities

Publication of materials before classes for students to consult, with explanations of concepts and examples. Resolution of exercises at the beginning of classes with quizzes to consolidate knowledge and with additional explanations.

 

Practical work using the indicated software.

Software

Power BI
Python

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	60,00
Trabalho escrito	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	27,00
Trabalho escrito	10,00
Trabalho laboratorial	20,00
Apresentação/discussão de um trabalho científico	3,00
Frequência das aulas	21,00
Total:	81,00

Eligibility for exams

According to the General Regulation for the Assessment of First Degreeand Master’s Degree students at the School of Economics and Management of the University of Porto all students enrolled in a course unit fulfill attendance requirements. (article 10th point 5).

Calculation formula of final grade

NF = 0.6 * NE + 0.4 * NT,

where NF, NE e NT denote, respectively, the final grade, the final exam grade and the term paper grade. NE>=7 (minimum grade).

For the NT component, there will also be a 10% weighting for classroom participation, which may be assessed in the form of quizzes.

 

Classification improvement

According to RAD (Student Assessment Regulations), article 18, it is not possible to use special seasons to improve the grade.

To improve the grade, a new registration is necessary to be included in the improvement of classification by frequency of the curricular unit.