Autenticação

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Programming

Code:

LTB11005

Sigla:

PROG

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2023/2024 - 1S

Ativa?	Yes
Página Web:	https://moodle.ips.pt/2324/course/view.php?id=1891
Unidade Responsável:	Departamento de Sistemas e Informática
Curso/CE Responsável:

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
LTB	52	Plano de Estudos	1	-	6	75	162

Língua de trabalho

Portuguese

Objetivos

The student will obtain knowledge, skills and proficiency in:

 

• To learn the conceptual foundations for developing and coding of algorithms in programming languages;
• To develop computer applications in high-level languages;
• Practical application of the course contents in real problems, using the procedural and imperative programming paradigm, and also the object-oriented programming paradigm;
• To develop logical and formal reasoning skills for solving complex problems.

Resultados de aprendizagem e competências

Upon completion of this curricular unit students will acquire the knowledge and skills necessary to:

• Apply their (acquired) logical and formal reasoning skills in solving complex problems.


• Develop algorithms and methods to solve real problems, particularly in the field of biomedical sciences.


• Implement software prototypes / informatics solutions applying recent programming paradigms and tools, using the Python language.

Modo de trabalho

Presencial

Pré-requisitos (conhecimentos prévios) e co-requisitos (conhecimentos simultâneos)

Programa

1. Basic concepts (5%)
   
   
   
   • Basic elements of a computer.
   
   
   • Information Representation.
   
   
   • Programming and problem solving.
   
   
   
   
   
   


2. Algorithms (5%)
   
   
   
   • Algorithms and programs.
   
   
   • Algorithm concept, bottom-up and top-down approaches
   
   
   
   


3. Programming Languages (5%)
   
   
   
   • Natural language, pseudocode, and programming languages concepts.
   
   
   • Low-level languages vs high-level languages.
   
   
   • Typed and untyped, compiled and interpreted languages.
   
   
   • Programs development phases.
   
   
   
   


4. Programming in a high-level, example-oriented language: Python (85%)
   
   
   
   • Python programming principles:
     
     
     
     • Program structure.
     
     
     • Expressions and Data Types.
     
     
     • Input and output statements.
     
     
     • Control structures.
     
     
     • Functions / Subprograms
     
     
     
     
   
   
   • Structured data types.
     
     
     
     • Arrays, Lists, dictionaries, tuples, sets
     
     
     
     
   
   
   • Introduction to object-oriented programming.
     
     
     
     • Class, object, and attributes.
     
     
     • Visibility and encapsulation
     
     
     • Inheritance and polymorphism
     
     
     • Introduction to exceptions handling.
     
     
     
     
   
   
   • Using external libraries.
   
   
   • Introduction to development tools (e.g., IDEs, Jupyter Notebooks, Google colab, github).
   
   
   • Processing data sequences (e.g., text files, Excel).
   
   
   • Introduction to Machine Learning.
   
   
   
   

Bibliografia Obrigatória

Andrew Bird, Dr Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade; The Python Workshop: Learn to code in Python and kickstart your career in software development or data science. 1st Edition., Packt Publishing, 2019. ISBN: 978-1-83921-885-9
Sebastian Raschka, Vahid Mirjalili; Python Machine Learning Third Edition, Packt Publishing, 2019. ISBN: 978-1-78995-575-0

Bibliografia Complementar

John V. Guttag; Introduction to Computation and Programming Using Python with Application to Understanding Data, Massachusetts Institute of Technology, 2016. ISBN: 9780262529624
Steven F. Lott, Dusty Phillips ; Python Object-Oriented Programming: Build robust and maintainable object-oriented Python applications and libraries, 4th Edition , Packt Publishing, 2021. ISBN: 978-1801077262
Mark Lutz; Programming Python, O’Reilly Media, 2011. ISBN: 978-0-596-15810-1
Python Software Foundation; The Python Tutorial (website (https://docs.python.org/3/tutorial/index.html))
Behrouz A. Forouzan; Foundations of Computer Science, 4th Edition, Cengage Learning EMEA, 2018. ISBN: 9781473751040

Métodos de ensino e atividades de aprendizagem

Software

Plataforma Anaconda-Python (Anaconda Distribution)
Visual Studio Code

Tipo de avaliação

Distributed evaluation with final exam

Componentes de Avaliação

Designation	Peso (%)
Defesa pública de dissertação, de relatório de projeto ou estágio, ou de tese	30,00
Teste	30,00
Trabalho laboratorial	20,00
Trabalho escrito	20,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	40,00
Estudo autónomo	45,00
Frequência das aulas	56,00
Trabalho escrito	4,00
Trabalho laboratorial	30,00
Total:	175,00

Obtenção de frequência

9,5/20
Note: Students who have won any of the TP (Theoretical - Practical) or LAB (Project + Laboratory) components, will be exempt from doing that component in the appeal season.

Fórmula de cálculo da classificação final

50% TP + 50% LAB (average> = 9.5)