Postgraduate Studies (Distance Learning) • 10 Months (Full-Time) • 20 Months (Part-Time)
ABOUT THE COURSE
Context
The rise of the internet and the digital transformation of society are events that have generated a large volume of data in various areas.
The Postgraduate Program in Computer Engineering with Data Analysis is not a management course, but rather a comprehensive training program focused on research and implementation, both at the technological and data levels. The course instructors are specialists and share up-to-date knowledge, ensuring that students are prepared for current work contexts.
During the program, students will acquire advanced knowledge in the areas of computer engineering and data analysis, and deepen the technological knowledge acquired in the first cycle (bachelor's degree).
Throughout the course, students will acquire the necessary knowledge to train future specialists in the field or work for top companies across Europe, developing analytical skills and critical thinking.
Professionals who graduate with distinction will be able to organize large volumes of data and develop solutions that allow them to leverage the full potential of that data. The Postgraduate Degree in Computer Engineering with Data Analysis opens doors to high-level positions in companies in Portugal or throughout Europe.
Objectives
By the end of the postgraduate program, the student should be able to:
- Working with the best professionals in the field in the development of multimedia information systems and software, and in research and data analysis projects.
- It is an essential element in any business, as it will help the company make decisions based on concrete data and not rely on intuition.
Target audience
This course is designed for professionals working in the field of computer science or who have an interest in engineering, computer science, and data analysis, and who hold a degree in similar fields, such as technology. No prior practical experience is required.
Curriculum Plan
| Course Unit | ECTS |
|---|---|
Data AnalysisIntroduction to the R and RStudio tools, covering installation, functionalities, and work environment. Programming in R: variables, data structures, functions, flow control, and iteration. - Data import, preparation, and manipulation processes using the tidyverse. - Visualization: grammar of graphics and the use of the ggplot2 package for information representation. - Exploratory data analysis and descriptive statistics methods, followed by concepts and applications of inferential statistics, including hypothesis testing. Fundamentals of machine learning: basic algorithms, limitations, and ethical implications of its use. |
6 |
Advanced DatabasesThe relational cost model: query processor, operators, and statistics. - Calculation of query plan costs and optimization in SQL The object-relational model: concepts and extensions in PostgreSQL and Oracle. - The object model: object-oriented schemas and queries in db4o - Queries in the object model: languages, execution, and performance analysis - The document model: fundamentals, modeling in MongoDB and PostgreSQL - Queries in the document model: languages, processing, and comparison between native and relational approaches. |
6 |
Mobile ComputingIntroduction to Ubicomp/IoT systems Wireless communication technologies IoT Protocols and Services Context and location Dynamic adaptation, energy management and safety. Embedded systems – Arduino with ZigBee/XBee - Embedded systems – LoPy with WiFi/BLE |
6 |
Artificial intelligenceIntroduction to Artificial Intelligence and Intelligent Agents Problem Solving, Research Not Specified Problem Solving, Informed Research, and Heuristics - Survey with Satisfaction of Restrictions - Research with Opponents and Games Introduction to Computer-Based Learning Machine Learning and Neural Networks |
6 |
Human-machine interactionHuman-Computer Interaction as a discipline of study Concepts of mediation in digital systems The development cycle in Human-Computer Interaction - Discovering user needs - Design alternatives in the development of Human-Computer Interaction Prototyping in Human-Computer Interaction Development Assessment in Human-Computer Interaction Development |
6 |
Mobile Application ProgrammingTechnologies and tools for developing mobile applications. Android development model with Kotlin and Android Studio. - Local SQLite databases and ORM (Room) HTTP communications and asynchronous programming Local Services (Intent Services, Services) - Viewing maps and interacting with Google Services Unit and integration testing of Android applications. |
6 |
Web Application ProjectWeb application architecture: models, design patterns, and selection criteria. Docker: installation, configuration, containers, images, and Docker Compose Backend with Golang and Gin: routes, handlers, data persistence, security. Frontend with React: configuration, components, state, forms, API consumption - Backend/frontend integration: communication, authentication/authorization with JWT Software testing in web applications - Deploying complete applications with Docker |
6 |
Project in Systems and NetworksIntroduction to Software Engineering - Software requirements specification - Software system modeling with UML - Development of backend applications using Node.js and Express. Integration of databases, routes, controllers, and forms. Test Automation and Test Driven Development Continuous integration, continuous delivery, and production deployment. |
6 |
Mobile Communications Networks and ServicesIntroduction to wireless communication technology - Data transmission in wireless networks Basic concepts of antennas and signal propagation in wireless networks. Terminal multiplexing techniques in wireless networks - Wireless local area network (WLAN) technologies Wireless network technologies for IoT (WPANs and LPWANs) Cellular Networks (WWANs) |
6 |
Computer VisionIntroduction and fundamentals of computer imaging and vision. Introduction to image processing and analysis in the spatial domain. Morphological image processing - Color image processing Image segmentation Representation and description of visual characteristics. - Object recognition and segmentation using machine learning (ML) |
6 |
Methodology
During the 8 weeks of each subject, students have the support of a subject matter expert who will answer questions, interact with students, and teach new content.
The main characteristic is flexibility; therefore, the study/interaction method is asynchronous, meaning the student manages their own time and study location, not being required to be available at a specific time. The 20 weekly hours are an average dedication for a student; no one is obligated to complete them (they will not be counted) and can be distributed as the student wishes. Ex.: 5 hours/day Monday to Friday. 10 hours/day Saturday and Sunday.
You can choose the course frequency in:
- Full-time (total duration of 10 months), attending two subjects at a time;
- or Part-time (total duration of 20 months), attending one subject at a time.
Every intake, New subjects begin approximately every two months.
The course content consists of videos, presentations, PDFs, readings from digital libraries, and other types of educational resources.
The LMS (platform) used at UFP is Canvas, one of the most modern LMS on the market.
Evaluation system
All modules/subjects have assessments.
Assessments are continuous, meaning they occur throughout the course and not just at the end. They consist of multiple-choice questionnaires, discussion forums, group projects, and scientific papers.
There is no need to write a dissertation or thesis at the end.
Conditions
Costs
Full-time: €700.00 x 5 intakes.
Part-time: €350.00 x 10 intakes.
(Total cost of the course: €3500.00)
Documents required for registration
- Diploma or certificate of academic qualifications: higher education graduation certificate or secondary education completion certificate (for those who are not graduates and need to use professional experience for application purposes);
- Certified copy of the Professional Association Card, if applicable;
- Curriculum Vitae (Europass Model);
- Supporting documents for the activities listed in the CV;
- Copy of civil and tax identification document (for issuing receipts).
Observations
- Since the course is 100% online, a residence permit is not mandatory, as there is no need to live in Portugal;
- Documents in English, French, and Spanish do not require official translation; documents in other languages do require official translation.;
- For candidates whose native language is not Portuguese, a document attesting to language proficiency with a minimum grade of B2 is required.
Contact
9th of April Square, 349
4249-004 Porto
T. +351 22 507 1300
academia@fundacaofernandopessoa.pt