- Details
- Category: Discipline
- Brandão, M. L. (2009). Manual para publicação científica: Elaborando manuscritos, teses e dissertações. Rio de Janeiro, Brasil: Elsevier. Artigos, livros, revistas e jornais científicos que estejam relacionados com a área/tema do trabalho de projeto.
- Business Processes and Operations Management
- 1860
- 13159
- Development Project
- ISLA Santarém1860-13159
- 3
- 12
- 0
- 25
- Não
- Português
- M1. The project supervision methodology will be the responsibility of the supervisor, but, as a general rule, it will consist of meetings (tutorial guidance) at a frequency appropriate to the work/student in question. M2. In addition, the supervisor may require the student to submit a set of interim reports as a further means of monitoring compliance with all the planned stages and tasks. M3. Reading of the recommended bibliography. This will serve as the basis for the preparation of the final report. At the end, the student must write a final report on the work carried out.
- Mandatory
- The objectives of the curricular unit are: O1. This course aims to develop the skills and abilities to develop a work of nature applied to an organizational context to ensure students a component of the application of knowledge and understanding to apply them in the context of specific activities of the respective professional profile. At the end of the curricular unit students should be able to: C1. Apply the techniques of development of work appropriate to the project; C2. Develop a project applying the techniques and methods appropriate to the respective context.
- 1. Project development following project management techniques. 2. Use the most appropriate tools and solutions for each situation. 3. Regular presentation of the project status. 4. Develop the project designed in the previous semester. 5. Preparation of the final project report. 5.1 AI as support for research and writing. 5.2 Reflection on scientific integrity. 6. Submission of the applied development project report 7. Presentation and discussion of the applied development project
Os estudantes podem entregar o relatório final de projeto em qualquer das épocas previstas:
a. Época de avaliação final
b. Época de recurso
c. Época especial
A avaliação é efetuada conforme previsto no regulamento geral de avaliação dos cursos de 1.º ciclo. Assim, o relatório final do projeto será apresentado perante um júri de que fará parte o/a Diretor/a de Curso, o/a docente orientador/a e um/a docente da área científica, nomeado para o efeito, que exercerá a função de arguente. Nos casos de impedimento do/a Diretor/a de Curso ou quando este/a seja o/a orientador/a do projeto, será nomeado outro/a docente do ciclo de estudos para integrar o júri.
O júri da defesa do trabalho final de ciclo de estudos delibera sobre a classificação final a atribuir, na escala numérica de 0 a 20 valores, sendo necessária uma nota mínima de 9.5 valores para aprovação à unidade curricular.
- Semestral
- .The course focuses on the development of an applied project in an organisational context, promoting the integration of acquired knowledge and the use of appropriate techniques and methods.
- Details
- Category: Discipline
- Marôco, João (2021). Análise Estatística com SPSS, ReportNumber. Schiefer, H., & Schiefer, F. (2021). Statistics for Engineers: An Introduction with Examples from Practice. Springer Nature. Devore, J. L., Berk, K. N., & Carlton, M. A. (2021). Modern mathematical statistics with applications. 3rd edition. New York: Springer. Rhinehart, R. R., & Bethea, R. M. (2022). Applied Engineering Statistics. CRC Press. Gupta, B. C., Guttman, I., & Jayalath, K. P. (2020). Statistics and probability with applications for engineers and scientists. Wiley.
- Engineering Work Safety
- 1210
- 979
- Statistics I
- ISLA Santarém1210-979
- 2
- 6
- 0
- 25
- Não
- Português
- The methodology used in classes will strike a balance between theoretical foundations and their practical application. Classes will address different topics and then demonstrate their practical application through a series of exercises inside and outside the classroom, encouraging active and independent participation by students. To this end, the expository method will be used to introduce concepts and structure reasoning, and the interrogative method will be used to assess learning in the theoretical part of each chapter. In the practical part, the demonstrative method will be used to provide practical examples of the content, as well as active, participatory, and autonomous methods to connect with each student's experience. Summary: Expository and Interrogative Methodology – participatory teaching, using motivational strategies and active pedagogical methodologies such as flipped classrooms, gamification, and problem/project-based learning whenever necessary.
- Mandatory
- The objectives of the curricular unit are: O1. To present the main concepts of descriptive statistics, probability and statistical functions; O2. Make known probability computation methods and their practical application in decision making; O3. Summarize and interpret univariate and bivariate data using software of statistical analysis; O4. Read and correctly interpret documents that make use of basic probabilistic and statistical language; O5. Formulate practical problems and express practical situations using the language of probability theory and statistics. At the end of the curricular unit students should be able to: C1. Analyze data by applying methods of descriptive statistics using software of statistical analysis; C2. Express situations of uncertainty relevant to decision making using probabilistic and statistical language; C3. Use statistical analysis software, such as SPSS and R Commander, and interpret the outputs resulting from the application of descriptive statistical methods.
- 1. Descriptive statistics 1.1. Basic concepts: population, statistical unit, attribute, and sample 1.2. Measurement scales of statistical data 1.3. Absolute and relative frequencies 1.4. Cumulative frequencies 1.5. Measures of location 1.6. Measures of dispersion: variance and standard deviation 1.7. Measures of skewness and kurtosis 2. Probability theory 2.1. Basic concepts 2.2. Axioms of probability 2.3. Conditional probabilities 2.4. Independent events 2.5. Multiplicative and additive rules 3. Random Variables 3.1. Discrete random variables: probability function, distribution function 3.2. Absolutely continuous random variables: probability density function, distribution function 3.3. Random Variable Parameters: expected value, variance and standard deviation 3.5. Independent random variables 4. Probability Distributions 4.1. Discrete distributions 4.2. Continuous distributions
Avaliação Curricular (*): O estudante realiza 2 fichas de exercícios em grupo e dois testes individuais. O estudante aprova se a classificação final for superior ou igual a 9.5 valores. A classificação final é calculada pela fórmula Classificação Final = 0.2*F1+0.3*T1+0.2*F2+0.3*T2, onde F1, F2, T1 e T2 denotam, respetivamente, as notas nas fichas de exercícios e nos testes individuais.
Avaliação Final: O estudante realiza o exame completo e aprova se obtiver classificação superior ou igual a 9.5 valores.
Época de Recurso e Época Especial: O estudante realiza o exame completo e aprova se obtiver classificação superior ou igual a 9.5 valores.- Semestral
- The course introduces the fundamental principles of probability and data analysis, developing skills to describe, interpret, and model quantitative information and support evidence-based decisions, with applications in occupational safety engineering and other scientific and technological areas.
- Details
- Category: Discipline
- Baptista, L. V., Nofre, J., & Jorge, M. D. R. (2018). Mobilidade, Cidade e Turismo: pistaspara analisar as transformações em curso no centro histórico de Lisboa. Sociologia, (tematico8), 14-32. Brasão, I. (2017). Hotel, os bastidores . Fundação Francisco Manuel dos Santos. Cohen, E. (2014). Contemporary Tourism: Diversity and Change . Routledge. Dias, R. (2003). Sociologia do Turismo . São Paulo: Editora Atlas. dos Santos Ericeira, R. C. (2020). Memória, identidade e comensalidade: a feijoada da família portelense . Memorandum: Memória e História em Psicologia, 37. Gaivão, B. (2017). Turista infiltrado: um olhar de fora para dentro . Fundação Francisco Manuel dos Santos.
- Tourism Management (ISLA Santarém)
- 2340
- 11993
- Sociology of Tourism
- ISLA Santarém2340-11993
- 1
- 5
- 0
- 25
- Não
- Português
- The pedagogical approach adopted is based on the active method, promoting cooperative (peer) and creative learning. Face-to-face: 1. Theoretical-practical teaching: Presentation of concepts and discussion of program content. 2. Laboratory practice: Field research in tourist destinations; Critical analysis of case studies related to the tourism phenomenon, and classroom debate. 3. Simulated practice: Scenarios and activities that allow students to explore real-world situations and solve problems related to tourism, applying sociological concepts and developing practical skills. Independent: 4. Consolidation of the content taught in class through additional research on the different topics covered and the development of supplementary work related to solving specific problems and group dynamics.
- Mandatory
- The objectives of the course are: O1. To understand the concept of Sociology of Tourism and its importance as a discipline; O2. Analyze the social interactions between different actors involved in the tourism phenomenon; O3. Relate tourism to the concept of social space; O4. Examine the importance of cultural identity and authenticity in the context of tourism; O5. Analyze ethical issues and discuss corporate social responsibility in the tourism sector. At the end of the course, students should be able to: C1. Apply sociological approaches to analyze the phenomenon of tourism and understand the social dynamics involved. C2. Manage tourist destinations in order to preserve and highlight their cultural identity and authenticity. C3. Recognize and promote awareness of social, ethical, and sustainable dynamics in the tourism sector.
A avaliação continua será composta pelos seguintes elementos:
Teste de avaliação nº 1 = 30%
Teste de avaliação nº 2 = 35%
Trabalho oral = 35%
- Semestral
- Details
- Category: Discipline
- Akshay, B. R., Pulari, S. R., Murugesh, T. S., & Vasudevan, S. K. (2024). Machine Learning: A Comprehensive Beginner's Guide. CRC Press. Liu, Y. H. (2024). Python machine learning by example. Packt Publishing Ltd. Barua, T., Hiran, K. K., Jain, R. K., & Doshi, R. (2024). Machine learning with python. Walter de Gruyter GmbH & Co KG. Harrison, M. (2019). Machine Learning-Guia de referência rápida: trabalhando com dados estruturados em Python. Novatec Editora.
- Artificial Intelligence
- 7052
- 24656
- Machine Learning
- ISLA Santarém7052-24656
- 1
- 4
- 0
- 25
- Não
- Português
- Mandatory
- 1. Build machine learning models in Python. 2. Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
- 1. Introduction to Machine Learning: 1.1. Definition 1.2. Applications 2. Unsupervised learning 2.1. Variable dimension reduction techniques 2.2. Clustering 2.2.1. Techniques for determining the number of clusters 2.2.2. Clustering models 2.3. Association rules 3. Supervised learning 3.1. Classification models 3.2. Regression models 4. Reinforcement learning
Os instrumentos de avaliação são uma frequência e um projeto. No projeto engloba um produto entregue no moodle que será analisado pelo docente e uma apresentação e entrega. As datas para a realização e entrega dos mesmos são disponibilizadas no moodle.
Descrição
Data limite
Ponderação
Frequência
dd-mm-yyyy
50%
Projeto **
dd-mm-yyyy
50%
Exame
dd-mm-yyyy
100%
Nota: Individual e em grupo**
- Semestral
- Details
- Category: Discipline
- Aires, L. M. (2013). Conceitos de Matemática - Fundamentos para as ciências da vida. Lisboa: Edições Sílabo. Luis, G. & Ribeiro, C. S. (1995). Álgebra Linear. Lisboa: McGraw-Hill de Portugal. Tan, S. T. (2010). Applied Mathematics: for the managerial, life, and social sciences (5th Ed). Brooks/Cole.
- Quality, Environment and Safety Management
- 1993
- 4944
- Quantitative Methods
- ISLA Santarém1993-4944
- 1
- 6
- 0
- 25
- Não
- Português
- The classes will address the concepts of the different themes and, later, show their practical application, with a series of exercises inside and outside the classroom, inducing the active and autonomous participation of students. For this purpose, the use of the expository method is foreseen to introduce the concepts and structure the reasoning and the interrogative method for the learning evaluation, in the theoretical aspect of each chapter. With regard to the practical aspect, the use of the demonstrative method is foreseen for the practical illustration of the contents, as well as active, participatory and autonomous methods to make the connection with the experience of each one. In summary: (1) Expository and Interrogative Methodology - participatory teaching, resorting whenever necessary to Motivation strategies and to (2) Active Pedagogical Methodologies such as Flipped Classroom, Gamification and Problem/Project Based Learning.
- Mandatory
- The objectives of the curricular unit are: O1. To develop students' reasoning skills; O2. To develop skills related to numerical patterns and relationships; O3. To introduce estimation and numerical calculation; O4. To present the basic principles of patterns and functions; O5. To present the principles of patterns in algebra, equations and inequalities; O6. To provide students with knowledge related to graphs and functions; O7. To develop skills related to limits and continuity of functions. At the end of the course, students should be able to: C1. Master the techniques associated with patterns, numerical relationships, estimation and numerical calculation; C2. Master the basic principles of patterns, functions and algebra; C3. Understand the fundamental concepts of graphs, functions, limits, continuity of functions and their applications; C4. Use mathematical language in the development of calculation techniques; C5. Apply calculation techniques in different situations.
- 1. Numerical Patterns and Relations 2. Numerical Estimation and Calculation 3. Patterns and Functions 4. From Patterns to Algebra - Equations 5. From Patterns to Algebra - Inequalities 6. Graphs and Functions 7. Limits and Continuity of Functions
Época Normal - Avaliação curricular (contínua) (*): O estudante realiza dois testes de avaliação individual. O estudante aprova se a classificação final for superior ou igual a 9.5 valores em 20 valores. A classificação final é calculada pela fórmula Classificação Final =0.50*T1+0.50*T2, onde T1 e T2 denotam, respetivamente, as notas nos testes de avaliação individual.
(*) A realizar no decorrer do semestre.
Época Normal - Avaliação final: O estudante realiza o exame completo e aprova se obtiver classificação superior ou igual a 9.5 valores em 20 valores.
Épocas de Recurso e Especial: O estudante realiza o exame completo e aprova se obtiver classificação superior ou igual a 9.5 valores em 20 valores.
Descrição
Data
Ponderação
Teste 1
4/11/2025
50%
Teste 2
20/01/2026 50% Exames
A agendar pelos serviços. 100% - Semestral
- The Quantitative Methods course plays a fundamental role in the areas of Science, Technology, Economics and Management, as is the case of the CTeSP Quality, Environment and Safety Management. Its main objective is to provide students with a solid foundation in essential mathematical concepts, preparing them for more advanced subjects. The course presents theoretical and practical tools that are essential for modeling and solving problems in different areas of knowledge. Students are expected to develop structured logical-mathematical reasoning, which will allow them to interpret, formulate and solve problems, creating a knowledge base that can be applied in subsequent subjects. The course encourages students' critical and analytical thinking with practical applications in order to contribute positively to their academic and professional future.
