Curricular Unit:Code:
Systems and Applications Seminar833SSAP
Year:Level:Course:Credits:
2MasterComputer Systems Engineering (Information Systems and Multimedia)4 ects
Learning Period:Language of Instruction:Total Hours:
Winter SemesterPortuguese/English52
Learning Outcomes of the Curricular Unit:
the subject theme is data and data science
The student must be able to:
- define the architecture and components of a complex application
- define the requirements of the components and select a development environment
- use patterns and good practice for the selected environments
- select the development tools suitable for the project
- select the suitable tools for version control
- define the testing phase for the application
Syllabus:
Introduction to data science, concepts, techniques and tools
This seminar integrates a broad set of knowledge from the course to apply it in a given project. It is required that students present a project with a significant level of complexity, including the initial project steps, used techniques, tools and frameworks to provide the application.
Students also need to prove their ability to deal with integrated tools for software development, version control, automatic testing, including interfaces, basecode and the use of databases
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
It is proposed a theme to serve as context to work on and to attack a real world problema.
Giving such a problema, the goal is to elaborate how it can be solved, according to project management practices and using system specification methodologies
Teaching Methodologies (Including Evaluation):
continuous
resolving a problem set and small projects that allow for a final report where a dataset is analysed.
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
this seminar intends to operacionalize how must be conducted an attack to a given problema that envolves the study and application of computational systems
Reading:
[1] Luis Borges Gouveia (2016). Ciência dos Dados. Universidade Fernando Pessoa (UFP).
[2] Gouveia, L. (2017). Uma breve introdução ao R: Exploração prática e exercícios. Manual prático, 68 páginas. Grupo *TRS, Tecnologia, Redes e Sociedade. Universidade Fernando Pessoa.
[3] Foster Provost, Tom Fawcett (2014). Data Science for Business. O'Reilly Media.