- Details
- Category: Discipline
- John V. Guttag, Introduction to Computation and Programming Using Python, 3rd Edition, ISBN: 9780262542364, MIT Press, 2021. Ernesto Costa, Programação em Python - Fundamentos e Resolução de Problemas (2.ª edição), FCA - Editora de Informática, 2024. Filipe Portela, Tiago C. Pereira, Introdução à Algoritmia e Programação com Python, FCA - Editora de Informática, 2023.
- Data Science
- 6321
- 2310
- Introduction to Programming
- ISMAT6321-2310
- 1
- 6
- 0
- 12
- Não
- Português
- In theoretical classes, the teacher presents and discusses the subjects of the discipline. In practical classes, students solve programming problems or perform longer works, with scripts, on the computer. Students will complete their training through individual or group assignments, carried out outside of classes. The evaluation corresponds to two practical evaluation works (20% + 20%) and one more theoretical-practical frequency (60%). The student will obtain approval if he / she has a grade equal to or higher than 9.5 values. The exam takes the form of a written test.
- Mandatory
- At the end of this curricular unit, approved students will have demonstrated that they are able to: - Understand the use of programming to solve problems within the scope of your study discipline. - Know the programming language used. - Reasonably master elementary programming techniques. - Solve simple programming problems independently. - Appreciate the algorithmic complexity of the programs they write. - Identify the main components of computer systems and their relationship with programming. - Recognize the main stages of the software development life cycle.
- - Introduction to Programming - Elements of the programs - Functions - Recursion - Iteration - Assertions - Basic algorithms - Fundamental data structures - Searches and sorting - Efficiency and order of growth of execution time - Memory usage - Classes - Object-oriented programming
A avaliação é composta pelas seguintes componentes:
Descrição
Data limite
Ponderação
Trabalho prático 1
A definir
20%
Trabalho prático 2
A definir
20%
Teste de avaliação
A definir
60%
- Semestral
- This course introduces students to the fundamentals of programming and their application to problem-solving within the field of Data Science. By the end of the course, students are expected to understand the role of programming in developing computational solutions, be familiar with the programming language used, master basic programming techniques, and be capable of independently solving simple problems. The course aims to foster an awareness of algorithmic complexity, an understanding of the relationship between key computational system components and programming, and recognition of the fundamental stages of the software development life cycle.
- Details
- Category: Discipline
- Sports Sciences
- 6154
- 22454
- Organization and Management of Sports Entities
- ISMAT6154-22454
- 3
- 6
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Design for Circular Economy
- 6334
- 23118
- The HUMAN Dimension of the Circular Economy
- ISMAT6334-23118
- 1
- 5
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- IT Engineering
- 587
- 7345
- Business-Systems Architecture
- ISMAT587-7345
- 2
- 6
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Santana, A.P. & Queiró, J. F. (2010). Introdução à Álgebra Linear. Gradiva.
- Data Science
- 6321
- 2091
- Linear Algebra
- ISMAT6321-2091
- 1
- 6
- 0
- 12
- Não
- Português
- The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises, with and without the use of a computer. Whenever appropriate, matrix computation and scientific computing software are used to support problem solving and the exploration of concepts.
- Mandatory
- At the end of this unit students should know: LO1. Master the concepts and elementary operations on matrices; LO2. Discuss and solve systems of linear equations and use the Gaussian elimination method; LO3. Formulate and solve real world problems using systems of linear equations; LO4. Calculate determinants and understand their utility; LO5. Determine eigenvalues and eigenvectors and know how to use them in the process of diagonalization; LO6. Recognize the concepts of vector space and linear transformation and use them to solve problems in these areas; LO7. Identify and use the contents addressed in solving Data Science problems.
- S1. Arrays S2 Systems of linear equations S3. Determinants S4. Eigenvalues and eigenvectors of matrices S5. Vector Spaces S6. Linear transformations
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. A avaliação contínua inclui a realização de três testes escritos com uma ponderação de 30% cada e a participação ativa nas aulas (10%).
- Semestral
- The curricular unit Linear Algebra belongs to the scientific area of Mathematics and provides the foundations of matrix algebra, vector spaces and linear transformations. These topics constitute an essential basis for several areas of Data Science, including machine learning, optimization, multivariate analysis and data processing.
