S1 Fundamentals and Agile Manifesto. Agile versus Waterfall
S2 Product Management vs Project Management. Two-Way Development: Product Discovery & Product Delivery
S3 Group project challenge. Challenge mechanics. Use-case proposals and outcomes
S4 Agile digital product teams
S5 Introduction to Minimum Viable Product - MVP and User Story Mapping (USM)
S6 Agile Methodologies Scrum and Kanban. Scrum rituals and artefacts: Product and Sprint Backlog, Epics, USM, Acceptance Criteria
S7 Definition of MVP for each use case and respective USM, using Miro: requirements and features of each step
S8 Prioritizing features: Impact vs Effort Matrix and MoSCoW
S9 Agile product development planning using Azure Boards: MVP delivery in weekly sprints
S10 Metrics to evaluate product effectiveness and efficiency. AARRR Funnel. Analysis in Google Analytics. User interviews. Retrospective and lessons learned. Demo day.

Course w/ periodic assessment. No Final Exam. Assessment weights:
• 70% Lab project carried out in a group + the final presentation and individual discussion.
• 30% 3 Mini-tests with multiple choice and um final test
A mark below 10 assigns the student to an exam in normal and/or the appeal period (30% of the mark), where the completion and approval of the group project or an individual project (70%) is mandatory.

Mandatory

Jeff Sutherland, J.J. Sutherland (2014) , Scrum: The Art of Doing Twice the Work in Half the Time
Darrell Rigby, Sarah Elk, Steve Berez, (2020) Doing Agile Right: Transformation Without Chaos Hardcover
Scrum Institute (2020) , The Scrum Framework 3rd Edition
www.scrum-institute.org/contents/The_Scrum_Framework_by_International_Scrum_Institute.pdf
Scrum Institute (2020) , The Kanban Framework 3rd Edition
www.scrum-institute.org/contents/The_Kanban_Framework_by_International_Scrum_Institute.pdf Artigos
Manifesto for Agile Software Development - https://agilemanifesto.org

Optional

Podcast
The Scrum Master Toolbox
https://scrum-master-toolbox.org - Spotify https://open.spotify.com/show/4r6DQLCHDaSNjbgtZtAfUp
The 5 minutes Product Manager
https://open.spotify.com/show/3JcR7uWeJ43wEJV1Tajprk?si=MtT67kWWRZGjGpuRhucHRw
The Agile Coach´s Corner
https://open.spotify.com/show/2jlYwMiw7W13pQ3ricLEaE?si=OaXpCEUXRGSU-qbyntIy6QArtigos ? Videos formativos
Agile Product Development With Scrum & Kanban
https://academy.productized.co/courses/agile-scrum-kanban/
? What is Agile and how it works?
? Introduction to Scrum in 7 Minutes
? Scrum: Doing Twice the Work in Half the Time | Jeff Sutherland | Book Review
? Essential Scrum Crash Course in 20 Minutes
? Kanban: from Toyota to Software Development in 2 Minutes
? Scrum vs Kanban - What's the Difference?
? Scrum vs Kanban - Two Agile Teams Go Head-to-Head and the winner is...?
? Practicing Agile in a Roadmap Culture | Mozilla Senior Product Manager| Arielle Kilroy
? Best Practices for Product Roadmap | Jeff Lash
? Agile Estimating and Planning using Planning Poker
? Writing Agile User Stories
? How to Write Good User Stories
? Splitting User Stories - Agile Practices
? Who is a Product Owner, Roles and Responsibilities?
? What´s a MVP - Minimum Viable Product
? Minimum Viable Product vs. Proof of Concept vs. Prototype
? MVP: Quickly Validate your Product
? 3 Awesome Minimum Viable Products (MVPs)
? Agile vs Waterfall, What's the Difference?

CP 1. Introduction to databases and information systems.

CP 2. Relational Model and data normalization.

CP 3. SQL language: data definition; data manipulation; programming routines and triggers.

CP 4. Data modeling with the UML language.

CP 5. Database design by converting UML to Relational Model;

CP 6. Transactions and competition.

CP 7. Introduction to database administration.

Assessment throughout the semester:

· 4 individual tests, each worth 20% of the student's final grade. Duration of each test: 1 hour. The last test is simultaneously with the 1st season exam.

· 20 individual worksheets carried out in the classroom, together worth 20% of the grade. There are 2 forms per class, one at the beginning and one at the end, except for the first and last classes.
There is no minimum grade. The student's grade is the weighted average of all assessments.

Assessment by exam:

· Individual written test, lasting 3 hours, worth 100% of the grade.

All tests are individual and to be carried out without external help, particularly without artificial intelligence. In case of suspicion of non-compliance with this rule, the student is called to take an oral test. In this case, if it appears that external help has been used, the test will be canceled and the incident will be reported to the Pedagogical Council.

Mandatory

Documentação da cadeira.
Ramos, P. “Desenhar Bases de Dados com UML, Conceitos e Exercícios Resolvidos”, Editora Sílabo, 2ª Edição, 2007.
Damas, L. “SQL - Structured Query Language”, FCA Editora de Informática, 14ª Edição, 2017.

Optional

Blaha, M.: "UML Database Modeling Workbook", Technics Publications, LLC, 2014.
Churcher, C., “Beginning Database Design: From Novice to Professional”, 2ª edição, Apress. 2012.
Ramakrishnan, R., Gehrke, J. “Database Management Systems”, 3ª edição, McGraw-Hill, 2003.

I. Introduction to Entrepreneurship;
II. Generation and discussion of business ideas;
III. Value Proposition Design;
IV. Business Ideas Communication;
V. Business Models Creation;
VI. Business Plans Generation;
VII. Minimum viable product (products, processes and services) test and evaluation;
VIII. Scalability analysis;
IX. Internationalization and commercialization;
X. Funding sources

Periodic grading system: - Group project: first presentation: 30%; second presentation: 30%; final report: 40%.

Mandatory

A. Osterwalder, Y. Pigneur / John Wiley & Sons, Value Proposition Design: How to Create Products and Services Customers Want, 2014, ·, ·
A. Osterwalder, Y. Pigneur / John Wiley & Sons, Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers., 2010, ·, ·
P. Burns / Palgrave Macmillan, Entrepreneurship and Small Business, 2016, ·, ·
S. Mariotti, C. Glackin / Global Edition. Pearson; Dorf. R., Byers, T. Nelson, A. (2014). Technology Ventures: From Idea to Enterprise. McGraw-Hill Education, Entrepreneurship: Starting and Operating A Small Business, 2015, ·, ·

Optional

SC1. Introduction to Cybersecurity: main components; cybersecurity pillars; cybersecurity frameworks.
SC2. Information Security Planning and Legal and Ethical Framework
SC3. Principles of Information Security Governance and Risk Management
SC4. Introduction to Information Security Technology: access controls, firewalls, vpns, idps, cryptography and other techniques.
SC5. Physical Security: physical access control mechanisms, physical security planning, among others.
SC6. Information Security Implementation: information security project management; technical and non-technical aspects of information security implementation.
SC7. Personnel Security: personnel security considerations; personnel security practices.
SC8. Maintenance of Information Security.

Assessment throughout the semester:
- Completion of a set of group projects and activities (50%) throughout the semester
- Completion of a set of individual mini-tests throughout the semester (50%) [minimum grade of 8 for each test]
For assessment throughout the semester, minimum attendance of 70% of classes is required.
Exam assessment:
For students who choose this process or who fail the assessment process throughout the semester, with 3 periods in accordance with the RGACC.

Mandatory

Whitman, M., Mattord, H. (2021). Principles of Information Security. Course Technology.
Whitman, M., & Mattord, H. (2016). Management of information security. Nelson Education.
Andress, J. (2014). The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice. Syngress.
Kim, D., Solomon, M. (2016). Fundamentals of Information Systems Security. Jones & Bartlett Learning.

Optional

Conjunto de artigos, páginas web e textos que complementam a informação bibliográfica da unidade curricular, e que serão fornecidos pela equipa docente.

S1 Classes and Objects
S2 Inheritance and polymorphism
S3 Abstract classes
S4 Interfaces and comparators
S5 Collections: lists, sets, maps
S6 Anonymous classes and lambdas
S7 Reading and writing files
S8 Exceptions and error handling
S9 Genericity and design patterns
S10 JUnit Tests and Documentation

CU with Periodic Assessment, not including a Final Exam:
8 individual assignments (10%), min grade of 9.5
Group project, with oral discussion (45%), min grade of 9.5
2 Mini-tests (45%), minimum grade of 9.5
If failing in the first period (< 10 out of 20), the student can retake the 1st and/or 2nd mini-tests (can also retake if scoring below the minimum grade in either or both) - accounting for 55% of the grade, with passing the Project or completing an individual project being mandatory - 45%

Mandatory

F. Mário Martins, "Java 8 POO + Construções Funcionais", FCA - Editora de Informática, 2017. ISBN: 978-972-722-838-6
Y. Daniel Liang, "Introduction to Java Programming: Comprehensive Version" 10th Ed. Prentice-Hall / Pearson, 2015.
Recursos Java http://java.sun.com

Optional

Ken Arnold, James Gosling e David Holmes, "The JavaTM Programming Language", 3ª edição, Addison-Wesley, 2000.
ISBN: 0-201-70433-1
Bruce Eckel, "Thinking in Java", 3ª edição, Prentice Hall, 2002. ISBN: 0-13-100287-2
Gamma, Helm, Johnson & Vlissides (1994). Design Patterns. Addison-Wesley. ISBN 0-201-63361-2.

CP1: Introduction to Business Intelligence – concepts, architecture, and tools.
CP2: Data integration and transformation – Power Query, M, connections to multiple sources.
CP3: Data modeling – relational tables, DAX, measures, and KPIs.
CP4: Data visualization – creating reports, dashboards, storytelling with data.
CP5: Assessment and best practices in BI projects – data ethics, governance, information quality.

Assessment throughout the semester includes:

- Practical project for developing an analytical information system carried out in a group (maximum of 3 students), including a report (15%) with a mandatory midterm submission (15%) and an oral defense with an individual grade (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- Weekly in-class and independent assignments (20%).

The use of AI tools (e.g., ChatGPT) is permitted for technical support and must be clearly referenced with the prompts used.

The Final Exam is a written, individual, closed-book exam covering all the material.

Those who do not opt ​​for assessment throughout the semester take the final exam in Period 1.

Those who did not successfully complete the assessment throughout the semester, or the exam in Period 1, with a score greater than or equal to 9.5 (out of 20), take the final exam in Period 2.

Mandatory

Greg Deckler, Learn Microsoft Power BI: A comprehensive, beginner-friendly guide to real-world business intelligence (2025)

R. Kimball, M. Ross The Data Warehouse Toolkit - the definitive guide to dimensional modeling, 3rd Edition. John Wiley & Sons, USA, (2013)

Optional

Será disponibilizada consoante os interesses dos estudantes e atualizações tecnológicas relevantes.

I. Development of the product/service image
II. Functionalities of the product/service
III. Development plan
IV. Development of the product/service (web/mobile or other)
V. Revision of the business plan
VI. Management and maintenance of the product/service
VII. Certification plan
VIII. Intellectual property, patents, and support documentation
IX. Main aspects for the creation of a startup - juridical, account, registry, contracts, social capital, obligations, taxes

Periodic grading system:
- Group project: first presentation: 30%; second presentation: 30%; final report: 40%. The presentations, demonstrations and Defence are in group.

Mandatory

·
A. Osterwalder, Y. Pigneur / John Wiley & Sons, Value Proposition Design: How to Create Products and Services Customers Want, 2014, ·, ·
A. Osterwalder, Y. Pigneur / John Wiley & Sons, Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, 2010, ·, ·
P. Burns / Palgrave Macmillan, Entrepreneurship and Small Business, 2016, ·, ·
R. Dorf, T. Byers, A. Nelson / McGraw-Hill Education, Technology Ventures: From Idea to Enterprise., 2014, ·, ·
S. Mariotti, C. Glackin / Global Edition. Pearson, Entrepreneurship: Starting and Operating A Small Business, 2015, ·, ·

Optional

S1 Introduction. The history of the Web. Programming languages for the Web; W3C standards.
S2 World Wide Web Architecture. Screen marking with HyperText Markup Language (HTML).
S3 Client-side programming. Structure description (HTML), style sheets (CSS) and dynamic updating of the graphical interface. Input validation; Introduction to client-side security.
S4 Server-side programming. Distribution of static content, dynamic generation of content and MVC design pattern. Services and communication between services. Introduction to server-side security.
S5 Data persistence. Integration with Database Management Systems ((SQL and NoSQL).
S6 Service-oriented web architectures. Web Services and Microservices. Middleware models for the Web. Containerization.

This course follows a semester-long project-based assessment model due to its predominantly practical nature and does not include a final exam.

Students are assessed based on the following components (A1 + A2):
A1: 3 individual multiple-choice mini-tests [30% - 10% each]. The A1 grade corresponds to the average of the 3 mini-test grades. Minimum grade of 8 out of 20.

A2: Group Project (4) and individual theoretical-practical discussion [70%]. The A2 grade corresponds to a percentage applied to the project grade, directly resulting from the performance demonstrated in the individual theoretical-practical discussion. Minimum grade of 9.5 out of 20.

Attendance:
Attendance is not mandatory.

Mandatory

Dean, J. (2025). Web programming with HTML5, CSS, and JavaScript. Jones & Bartlett Learning, 2nd Edition. ISBN: ‎ 9781284302967.

Percival, H., & Gregory, B. (2020). Architecture Patterns with Python: Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices. O'Reilly Media. ISBN: 9781492052203

Richardson, L., & Amundsen, M. (2013). RESTful Web APIs. O'Reilly Media. ISBN: 9781449358068.

Mozilla Foundation. (n.d.). MDN Web Docs. https://developer.mozilla.org/

Ramírez, S. (n.d.). FastAPI documentation. https://fastapi.tiangolo.com/

Docker Inc. (n.d.). Docker documentation. https://docs.docker.com/

Optional

Fielding, R. T. (2000). Architectural styles and the design of network-based software architectures (Doctoral dissertation, University of California, Irvine). https://ics.uci.edu/~fielding/pubs/dissertation/top.htm

Haverbeke, M. (20). Eloquent JavaScript: A Modern Introduction to Programming (4th ed.). No Starch Press. ISBN: 9781593279509. Available at https://eloquentjavascript.net/

Martin, R. C. (2025). Clean Code: A Handbook of Agile Software Craftsmanship. Prentice Hall. 2nd Edition. ISBN: 9780135398579.

PocketBase Authors. (n.d.). PocketBase Documentation. https://pocketbase.io/docs/

React Team. (n.d.). React Documentation. https://react.dev/

MariaDB Foundation. (n.d.). MariaDB Server Documentation. https://mariadb.com/docs/

MongoDB, Inc. (n.d.). MongoDB Documentation. https://www.mongodb.com/docs/

World Wide Web Consortium. (2004). Architecture of the World Wide Web, Volume One. W3C Recommendation. https://www.w3.org/TR/webarch/

S1: Essential AI concepts: definition, historical evolution, and multidisciplinary relevance.
S2: Predicate logic and knowledge-based systems: representation, deduction, and applications.
S3: Reasoning methods and heuristics: informed and uninformed search, selecting suitable strategies.
S4: Introduction to machine learning: basic notions, classification and regression algorithms, evaluation metrics.
S5: Simple predictive models: design, implementation, and results analysis in real-world scenarios.
S6: Generative AI: foundations, prompt engineering, and creative applications.
S7: Critical approach to AI limitations, ethical challenges, and socio-economic implications.

The assessment of this curricular unit follows the model of Assessment Throughout the Semester (ALS), provided for in the RGACC, and includes several moments, organized into three assessment blocks (AB), aimed at measuring progress and consolidating knowledge. It is made up as follows:
AB1: Written Exam (40%)
Assesses the fundamental concepts of artificial intelligence, machine learning, and generative AI. The exam evaluates both theoretical understanding and the ability to apply the concepts in practice.

AB2: Final Project in AI (60%)
Conducted individually or in groups, this project involves the design, implementation, and evaluation of a prototype or case study based on the course content. It encourages integration of knowledge and critical reflection on the topics covered. Evaluation is divided into a written report (60%) and an oral presentation (40%), delivered during the final session.

Additional rules:
Each assessment block requires a minimum score of 8.5 points.
If necessary, an individual oral discussion may be conducted to confirm knowledge.
The final course grade is the weighted sum of both blocks, with a minimum of 10 points required to pass.

Mandatory

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
→ Considerado a "bíblia" da IA, cobre desde fundamentos históricos até algoritmos modernos.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
→ Introdução acessível e aprofundada ao deep learning.

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
→ Livro clássico sobre fundamentos de aprendizagem de máquina.

Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
→ Introdução mais popular, sem fórmulas, ideal para contexto geral.

Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning.
→ Didático, foca em exemplos práticos com Keras/TensorFlow.

Optional

Haenlein, M., Kaplan, A., Tan, C. T., Tan, B. C., & Zhang, P. (2019). Artificial Intelligence (AI) and Management Analytics. Journal of the Academy of Marketing Science, 48(3), 411–431.

Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255–260.

Topol, E. (2019). High-performance Medicine: The Convergence of Human and Artificial Intelligence. Nature Medicine, 25, 44–56.

Syllabus contents (SC):
SC1 - Descriptive statistics: Types of variables. Frequency tables and graphical representations. Central tendency measures. Measures of spread and shape.
SC2 - Concepts of probability theory: definitions, axioms, conditional probability, total probability theorem and Bayes's formula
SC3 - Univariate and bivariate random variables: probability and density functions, distribution function, mean, variance, standard deviation, covariance and correlation.
SC4 - Discrete and Continuous distributions: Uniform discrete and continuous, Bernoulli, binomial, binomial negative, Poisson, Gaussian, Exponential Chi-Square and t distribution.
SC 5 - Sampling: basic concepts. Most used sample distributions
SC6 - Point estimation and confidence intervals
SC7 - Hypothesis testing: types of errors, significance level and p-value

Approval with a mark of not less than 10 in one of the following methods:
- Assessment throughout the semester: 1 mini-test taken during the lessons (15%) + Final written test taken on the date of the 1st period (60%) + autonomous work (5%) + group project (20%),
All assessment components, except for the autonomous work, are mandatory and require a minimum grade of 8 out of 20.
or
- Assessment by Exam (100%).

Notes:
1) A complementary oral assessment may be conducted after any evaluation moment to validate the final grade.
2) For this Course Unit, students must consult the Iscte Academic Code of Conduct, available on the institutional platforms, to ensure compliance with the established ethical and behavioral standards.

Mandatory

E. Reis, P. Melo, R. Andrade & T. Calapez (2015). Estatística Aplicada (Vol. 1) - 6ª ed, Lisboa: Sílabo. ISBN: 978-989-561-186-7.
Reis, E., P. Melo, R. Andrade & T. Calapez (2016). Estatística Aplicada (Vol. 2), 5ª ed., Lisboa: Sílabo. ISBN: 978-972-618-986-2.
Afonso, A. & Nunes, C. (2019). Probabilidades e Estatística. Aplicações e Soluções em SPSS. Versão revista e aumentada. Universidade de Évora. ISBN: 978-972-778-123-2.
Ferreira, P.M. (2012). Estatística e Probabilidade (Licenciatura em Matemática), Instituto Federal de Educação, Ciência e Tecnologia do Ceará – IFCE III, Universidade Aberta do Brasil – UAB.IV. ISBN: 978-85-63953-99-5.
Farias, A. (2010). Probabilidade e Estatística. (V. único). Fundação CECIERJ. ISBN: 978-85-7648-500-1.

Optional

Haslwanter, T. (2016). An Introduction to Statistics with Python: With Applications in the Life Sciences. Springer. ISBN: 978-3-319-28316-6.

CP1 - Introduction to AI and Generative AI:
* Theoretical exposition on the historical context, evolution and important concepts about Artificial Intelligence (AI) and Generative AI
CP2 - Prompt Engineering:
* Explanation of good practices for interacting with generative language models
CP3 - Generative AI Tools:
* Exploration of multiple Generative AI tools, based on text, images and videos
CP4 - Formation of argumentative content:
* Development of creative solutions using argumentation practices and Generative AI tools
CP5 - Rules for scientific writing:
* Application of citation and referencing standards (APA standards) in academic writing

Assessment throughout the semester includes the following activities:
1. Individual activities (50%): will include assessment elements related to prompt engineering, the responsible use of Generative AI, and also oral defense in the group presentation.
2. Group activities (50%) [ students are organized into groups of up to 5 members, formed randomly]: will include the creation, revision, editing, and validation of AI-generated content, as well as the appropriate, strategic, and responsible use of generative AI tools and techniques, throughout the group work during classes, culminating in a presentation and peer review session in the final class.

General Considerations:
Given the theoretical-practical and applied nature of the UC, the assessment takes place in the form of assessment throughout the semester, with assessment by exam not being foreseen. Assessment in the 2nd season and special season will have an alternative assessment method, so any students wishing to carry out the assessment in these seasons must first contact the teacher to be informed of the assessment procedure.

For assessment purposes throughout the semester, attendance and participation in at least 80% of classes and having more than 7 points in each of the assessments is mandatory. Approval requires a minimum final grade of 10 points and compliance with the attendance and participation requirement mentioned above.
The group will have to ensure that there is at least one computer for each group to allow activities to be carried out in the classroom.

The curricular unit coordinator may determine the holding of a complementary oral discussion regarding any assessment element/instrument provided for in this Curricular Unit Sheet, whenever this proves necessary to confirm the acquisition of the knowledge and skills assessed, or clarify discrepancies between different assessment elements.

The complementary oral discussion does not constitute an autonomous assessment instrument, does not have its own weight in the final classification of the curricular unit and is intended exclusively for validating the classification of the respective assessment element/instrument. The classification previously assigned to this element/instrument is provisional in nature until the complementary oral discussion is carried out, and may be confirmed or revised depending on the validation carried out and the applicable evaluation criteria.

Mandatory

Ribeiro, A. & Rosa, A. (2024). Descobrindo o potencial do CHATGPT em sala de aula: guia para professores e alunos. Atlantic Books.
Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in education and teaching international, 61(2), 228-239.
d’Alte, P., & d’Alte, L. (2023). Para uma avaliação do ChatGPT como ferramenta auxiliar de escrita de textos acadêmicos. Revista Bibliomar, São Luís, 22(1), 122-138. DOI: 10.18764/2526-6160v22n1.2023.6.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.

Optional

Cowen, T., & Tabarrok, A. T. (2023). How to learn and teach economics with large language models, including GPT. GMU Working Paper in Economics No. 23-18. DOI: 10.2139/ssrn.4391863
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing.

PC1. Function. Elementar functions, Different type of functions. Operations with functions. Logaritmic and trigonometric functions.
PC2. Limits of a function at a point, Continuity at a point. Assimptotic lines.
PC3. Derivative of a function at a point. Derivative rules. Optimization problems.
PC4. Derivative of composed functions – chain rule. Derivative of the inverse function.
PC5. Linear approximation and Taylor approximation.
PC6. Solving linear equation systems. Matrices and operations. Inverting matrices. Determinants and properties. Linear transformations.
PC7. Real vector space. Inner product. Parallelism and perpendicularity.
PC8. Eigenvalues, eigenvectores and matrix diagonalization.

Approval requires a final grade of no less than 10 points (out of 20) in one of the following modalities:
- Continuous Assessment:
* 3 tests (T1, T2, T3), with a minimum score of 7.0 points (out of 20), weightint 30% each
* autonomous work in moodle, with a weight of 5%
* critical reflection report on the use of Artificial Intelligence throughout the semeste, with a weight of 5%
* T1 and T2 are taken during the semester
* T3 is taken on the date of the 1st exam period
* T1 or T2 may be retaken on the date of the 1st exam period
or
- Final Exam Assessment (100%).

Oral examinations may be conducted if the instructor deems it appropriate.
Grades above 17 points must be defended in an oral exam.

The Course Unit Coordinator may require a supplementary oral discussion regarding any assessment component/instrument provided for in this Course Unit Description whenever deemed necessary to confirm the acquisition of the knowledge and competencies being assessed, or to clarify discrepancies between different assessment components.

The supplementary oral discussion does not constitute an independent assessment instrument, carries no separate weighting in the final course unit grade, and is intended exclusively to validate the grade awarded for the respective assessment component/instrument. The grade previously assigned to that component/instrument shall be considered provisional until the supplementary oral discussion has taken place and may be confirmed or revised in light of the validation conducted and the applicable assessment criteria.

The student shall be notified of the supplementary oral discussion at least 24 hours in advance, with the date and time of the discussion specified in the notification. Failure to attend, without a valid justification, shall render validation of the respective assessment component/instrument impossible, and the consequences applicable to its non-completion or withdrawal shall apply.

Mandatory

Stewart, J. (2022). Cálculo, Vol I, Cengage Learning, (9a Ed.)
Cabral I., Perdigão, C. e Saiago, C. (2018). Álgebra Linear: Teoria, Exercícios Resolvidos e Exercícios Propostos com Soluções, Escolar Editora
Magalhães, L.T. (2004). Álgebra Linear como Introdução a Matemática Aplicada, 8ª edição, Texto Editora

Optional

Campos Ferreira, J. (2018). Introdução à Análise Matemática, Fundação Calouste Gulbenkian
Goldstein, L. (2011). Matemática Aplicada a Economia. Administração e Contabilidade, (12a edição) Editora Bookman
Strang, G., (2007) Computational Science and Engineering, Wellesley-Cambridge Press.

S1 Historical evolution of computers, human-computer interaction, impact on modern computing; von Neumann architecture: model, characteristics, limitations; alternatives.
S2 Binary representation, data types, instruction and program storage; code translation: programming languages, compilers, assembly.
S3 Instruction types and formats, addressing modes, memory access, execution cycle; control flow, data transfer.
S4 CPU, GPU, and Peripherals: CPU structure and components (ALU, CU, registers), internal organisation, data and control flow, pipeline concepts; introduction to GPU; motherboard, communication ports, peripherals.
S5 CPU-memory interaction, storage hierarchy, cache (mapping, replacement), main memory, persistent storage (HDDs, SSDs), virtual memory.
S6 Performance metrics (CPI, MIPS, FLOPS), factors influencing performance, analysis and optimisation of memory and processor usage, role of the operating system.

The assessment can be carried out in one of the following modes:

Assessment throughout the semester:

Two written tests during the semester, with a minimum grade of 8 out of 20 for each test (30% + 30%).

Group practical project and its oral presentation (40%).

Once the practical group work has been completed, if the student does not obtain the minimum grade in the written assessment tests, they may take the theoretical component assessment in an exam worth 60%.

Final Exam Assessment:

Written exam (100%).

To pass the course unit, students must achieve a final grade of 9.5 or higher out of 20.

Mandatory

Morris Mano, M., & Ciletti, M. D., ""Computer System Architecture"", Pearson, 6ª edição (2017). Stallings, W., ""Computer Organization and Architecture: Designing for Performance"", Pearson, 10ª edição (2015). Guilherme Arroz, José Monteiro, Arlindo Oliveira, Arquitectura de Computadores: dos Sistemas Digitais aos Microprocessadores – 6.ª Edição, IST Press, 2024, ISBN: 978-972-8469-54-2."

Optional

S1. Introduction to Design Thinking

S2. Stage 1: Empathize

S3. Stage 2: Define the Problem

S4. Stages 3 and 4: Ideate and Prototype

S5. Stage 5: Test and Feedback

S6. Critical Reflection (2 hours)

The course unit adopts an active, project-based learning approach and is assessed through continuous assessment throughout the semester:

Class participation (20%): Assesses students' attendance, engagement, and contribution to classroom discussions and learning activities.
Individual project (40%): Students will develop an individual project applying the Design Thinking process to address a specific problem. Assessment will consider the application of the Design Thinking stages, the quality of the proposed solutions, and the level of creativity demonstrated.
Group project (40%): Students will work in teams to develop a collaborative project applying the Design Thinking process to address a real-world challenge. Assessment will be based on the application of the Design Thinking stages, the quality of the proposed solutions, and the effectiveness of teamwork and collaboration.

For all submitted assignments, the use of artificial intelligence (AI) tools must be transparent, explicitly declared, and compliant with Iscte's academic integrity regulations, through a signed declaration attached to the submitted work. Failure to provide this declaration or to disclose the use of AI technologies constitutes an academic misconduct offence under the Iscte Student Disciplinary Regulations.

Given the theoretical-practical and applied nature of the course unit, assessment is conducted exclusively through continuous assessment during the semester, and no final examination is offered. The final assessment of written assignments may include an individual oral examination to clarify or validate the submitted work.

Assessment during the resit period and the special assessment period will follow an alternative assessment format. Students wishing to be assessed during these periods must contact the course lecturer in advance to obtain information about the applicable assessment procedures.

For continuous assessment, students are required to attend and actively participate in at least 80% of the scheduled classes.

Mandatory

Brown, T. (2008). Design Thinking. Harvard Business Review, 86(6), 84–92.
Lewrick, M., Link, P., & Leifer, L. (2018). The design thinking playbook: Mindful digital transformation of teams, products, services, businesses and ecosystems. John Wiley & Sons.
Lockwood, T. (2010). Design Thinking: Integrating Innovation, Customer Experience and Brand Value. Allworth Press.
Stewart S.C (2011) “Interpreting Design Thinking”. In: https://www.sciencedirect.com/journal/design-studies/vol/32/issue/6

Optional

Brown, T., & Katz, B. (2011). Change by design. Journal of product innovation management, 28(3), 381-383.
Brown, T., Katz, B. M. Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. HarperBusiness, 2009.
Liedtka, J. (2018). Why Design Thinking Works. Harvard Business Review, 96(5), 72–79.
Gharajedaghi, J. (2011). Systems thinking: Managing chaos and complexity. A platform for designing business architecture. Google Book in: https://books.google.com/books?hl=en&lr=&id=b0g9AUVo2uUC&oi=fnd&pg=PP1&dq=design+thinking&ots=CEZe0uczco&sig=RrEdhJZuk3Tw8nyULGdi3I4MHlQ

PC1. Integrated development environments. Introduction to programming: Logical sequence and instructions, Data input and output.
PC2. Constants, variables and data types. Logical, arithmetic and relational operations.
PC3. Control structures.
PC4. Lists and Lists of Lists
PC5. Procedures and functions. References and parameters.
PC6. Objects and object classes.
PC7. File Manipulation.

This course follows a semester-long project-based assessment model due to its predominantly practical nature and does not include a final exam.

Students are assessed based on the following components (A1 + A2 + A3):
A1: 3 Individual Learning Tasks with teacher validation [15% - 5% each]
Three learning tasks will be completed throughout the semester.
The A1 grade corresponds to the average of the grades from the 3 tasks. Minimum grade of 8 out of 20.

A2: ePortfolio [15%]. Challenges will be proposed throughout the semester, including activities to be carried out outside the classroom context (participation in workshops, seminars, or training). The A2 grade corresponds to the average of the grades calculated based on the number of challenges proposed. No minimum grade.

A3: Group Project (3) and individual theoretical-practical discussion [70%]
The A3 grade corresponds to a percentage applied to the project grade, which directly results from the performance demonstrated in the individual theoretical-practical discussion. Minimum grade of 9.5 out of 20.

Attendance:
Attendance is not mandatory.

Mandatory

Portela, F., & Pereira, T. (2023). Introdução à Algoritmia e Programação com Python. FCA. ISBN: 9789727229314.

Sobral, S. R. (2024). Introdução à Programação Usando Python (2.ª ed.). Edições Sílabo. ISBN: 9789895613878.

Downey, A. B. (2024). Think Python: How to Think Like a Computer Scientist (3rd ed.). O'Reilly Media. ISBN: 9781098155438.

Python Software Foundation. Python 3 Documentation. Disponível em: https://docs.python.org/3/

Optional

Menezes, N. N. C. (2019). Introdução à Programação com Python: Algoritmos e Lógica de Programação para Iniciantes. Novatec Editora. ISBN: 9788575227183.

Matthes, E. (2023). Python Crash Course (3rd ed.). No Starch Press. ISBN: 9781718502703.

Martins, J. P. (2015). Programação em Python: Introdução à Programação com Múltiplos Paradigmas. IST Press. ISBN: 9789898481474.

Reitz, K., & Schlusser, T. (2016). The Hitchhiker's Guide to Python: Best Practices for Development. O'Reilly Media. ISBN: 9781491933176.
Disponível em: https://docs.python-guide.org/

Beazley, D., & Jones, B. K. (2013). Python Cookbook: Recipes for Mastering Python (3rd ed.). O'Reilly Media. ISBN: 9781449340377.

PC1. Preparing for a presentation.
PC2. Non-verbal communication techniques.
PC3. Voice and body communication, audience involvement. PC4. Presentation practice and feedback. The learning objectives will be achieved through practical and reflective activities, supported by the active and participatory teaching method which emphasizes experiential learning. Classes will consist of activities such as: Theatrical experiences and group discussions; Practical activities; Presentations and exhibitions of autonomous work; Individual reflection.

The assessment of the Public Presentations with Theatrical Techniques course aims to gauge the development of students' skills in essential aspects of public presentations. The assessment structure includes activities covering different aspects of the experiential learning process involving both theatrical techniques and specific communication techniques.
Assessment throughout the semester includes activities covering different aspects of the process of preparing a public presentation, including group and individual work activities:
Group activities (50%) [students are challenged to perform in groups of up to 5 elements, made up randomly according to each activity proposal].
1-Practical Presentations: Students will be assessed on the basis of their public presentations throughout the semester:
Description: each group receives a presentation proposal and must identify the elements of the activity and act in accordance with the objective.
The results of their work are presented in class to their colleagues (Time/group: presentation - 5 to 10 min.; reflection - 5 min.). Assessment (oral): based on active participation, organization of ideas and objectivity in communication, vocal and body expression, the use of theatrical techniques and performance. Presentations may be individual or group, depending on the proposed activities.
Individual activities (50%)
1-Exercises and Written Assignments (Autonomous Work):
Description: In addition to the practical presentations, students will be asked to carry out exercises and written tasks related to the content covered in each class. These activities include reflecting on techniques learned, creating a vision board, analyzing academic objectives, student self-assessment throughout the semester, answering theoretical questions and writing presentation scripts.
Assessment: (Oral component and written content), organization, content, correct use of the structure and procedures of the autonomous work proposed in each class, ability to answer questions posed by colleagues and the teacher. Communication skills and the quality of written work will be assessed, with a focus on clarity of presentation. These activities will help to gauge conceptual understanding of the content taught.
There will be no assessment by final exam, and approval will be determined by the weighted average of the assessments throughout the semester.
General considerations: in the assessment, students will be given feedback on their performance in each activity.
To complete the course in this mode, the student must attend 80% of the classes. The student must have more than 7 (seven) points in each of the assessments to be able to remain in evaluation in the course of the semester.

Mandatory

Prieto, G. (2014). Falar em Público - Arte e Técnica da Oratória. Escolar Editora.

Optional

Anderson, C. (2016). TED Talks: o guia oficial do TED para falar em público. Editora Intrinseca.
Luiz, P. (2019). Manual de Exercícios Criativos e Teatrais. Showtime. Rodrigues, A. (2022). A Natureza da Atividade Comunicativa. LisbonPress.

S1: Introduction, Program, and Assessment. Computing and HCI: History, state-of-the-art, and applications
S2: User-centered design process.
S3: User and task analysis. Empathy map. Personas. User "as is" scenarios and journeys. User questions. User requirements.
S4: We, the Humans
S5: Principles and golden rules of interface design. Usability
S6: Visual design of screens (WWW, mobility)
S7: Ideation. Storyboards. Prioritization. Low-fidelity (Lo-Fi) and high-fidelity (Hi-Fi) prototypes of the solution
S8: Deliver a functioning solution. Heuristic evaluation with experts. User evaluation. Statistical analysis of evaluation data. Calculate metrics and iterate the design. Requirements of an MVP (Minimum Viable Product). Elevator Pitch to investors and users

Given its eminently practical and applied nature, this curricular unit follows a project-based model — Assessment Throughout the Semester — as provided for in the RGACC, Iscte’s General Regulations for the Assessment of Knowledge and Competences. It does not include assessment by final examination and comprises the following assessment components:

Mini-test 1 – 15%
Mini-test 2 – 15%
D1 Project Proposal Document – 4%
D2 User Requirements Document – 12%
D3 Version 1 Low-Fidelity Paper Prototype (Lo-Fi) – 10%
D4 Expert Evaluation Report on the Lo-Fi Prototype – 12%
D5 High-Fidelity Prototype (Hi-Fi) and Report – 10%
D6 User Evaluation Report on the Hi-Fi Prototype – 11%
D7 Oral Presentation of the Project – 11%

Mandatory minimum attendance in 2/3 of classes.

Use of artificial intelligence. The Guidelines for the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026) apply, under the following specific arrangements: the use of artificial intelligence is permitted in project deliverables and must be declared in accordance with Section 4 of the Guidelines, including completion of the attached form; its use is prohibited in tests and practical assessments conducted in class, as well as in any repeat assessment of these components.
For any assessment component, the lecturer or curricular unit coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or assess the knowledge and competences demonstrated by the student.
The assessment includes individual oral verification components in which students are required to explain and modify the work submitted.

To pass the curricular unit, students must achieve a minimum final mark of 10 out of 20, calculated as the weighted sum of all assessment components.

A student will fail this curricular unit if they do not pass the Assessment Throughout the Semester (ALS).

In the second Exam period, mini-tests with a score of ≤7.5 must be retaken. If the student fails the regular period (score <10), is eligible to retake the 2 mini-tests in the 2nd or special seasons (30% of the grade). It ismandatory to pass the group project (70% of the grade).

Mandatory

Brown, T (2009), Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, HarperCollins, 2009, ISBN-13: 978-0062856623
Lewrick, M, Link, P., Leifer, L. (2020). The Design Thinking Toolbox, Wiley, ISBN 9781119629191
Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N., Nicholas Diakopoulos, N. (2017). Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th edition), Pearson, ISBN-13: 978-0134380384
Manuel J. Fonseca, Pedro Campos, Daniel Gonçalves (2017), Introdução ao Design de Interfaces, FCA, Portugal, 2017, 3ª Edição,
Norman, D. (2013). The Design of Everyday Things, Revised and Expanded Edition. MIT Press. ISBN: 9780262525671
Nielsen, J., Mack, R. (1994). Usability Inspection Methods 1st Edition. John Wiley & Sons.

Optional

Johnson, J. & Henderson, A. (2002). Conceptual models: begin by designing what to design. Interactions. 9, 1: 25-32. https://dl.acm.org/doi/10.1145/503355.503366
Joseph J. LaViola Jr., Ernst Kruijff, Ryan P. McMahan, Doug Bowman, Ivan P. Poupyrev (2017), 3D User Interfaces: Theory and Practice (2nd Edition), Addison-Wesley Professional, ISBN-10: 0134034325.
Yvonne Rogers, Helen Sharp, Jenny Preece (2011), Interaction Design: Beyond Human-Computer Interaction, 3rd edition, Wiley, ISBN-13: 978-0470665763
? Snyder, C. (2003). Paper Prototyping: the fast and easy way to design and refine user interfaces. Morgan Kaufmann Publishers.
The Basics of User Experience Design by Interaction Design Foundation, https://www.interaction-design.org/
Artigos:
Nielsen, J. (1994) Enhancing the explanatory power of usability heuristics. Proc. ACM CHI'94 Conf. (Boston, MA, April 24-28), pp. 152-158.
Rettig M. (1994), Prototyping for Tiny Fingers, Communications of The ACM, 1994

S1. The Union-Find data structure
S2. Algorithm analysis
S3: Data structures: stacks, queues, lists, bags
S4: Elementary sorting: selectionsort, insertionsort, shellsort
S5: Advanced sorting: mergesort, quicksort, heapsort
S6. Complexity of sorting problems
S7: Priority Queues
S8. Elementary symbol tables
S9. Binary search trees
S10. Balanced search trees
S11. Hash tables

Period 1: Assessment throughout the semester or Final Exam
Assessment throughout the semester, requiring attendance at least 3/4 of the classes:
- 2 practical tests (60%), with a minimum grade of 7.5 in each.
- 2 theoretical tests (40%), with a minimum grade of 7.5 in each.
The final weighted average between the theoretical and practical tests must be equal to or higher than 9.5.
Assessment by Exam:
- (100%) Final Exam with theoretical and practical components
Students have access to the assessment by Exam in Period 1 if they choose it at the beginning of the semester or if they fail the assessment throughout the semester.
Period 2: Final Exam
- (100%) Final Exam with theoretical and practical components
Special Period: Final Exam
- (100%) Final Exam with theoretical and practical components

Mandatory

Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C. (2022). Introduction to Algorithms, Fourth Edition. Estados Unidos: MIT Press.
Portela, F., Pereira, T. (2023). Introdução à Algoritmia e Programção com Python. Portugal: FCA
Sedgewick, R., Wayne, K. (2014). Algorithms, Part II. Reino Unido: Pearson Education.

Optional

PC1 Sequences. Monotony. Bounded sequences. Geometric progression
PC2 Convergence of sequences
PC3 Numerical series, partial sums and sum
PC4 Convergence criteria of series of non-negative terms
PC5 Simple and absolute convergence of alternating series. Leibniz's criterion
PC6 Power series and domain of convergence
PC7 Antiderivatives. Riemann definite integral. Fundamental theorem of calculus
PC8 Integration by parts and change of variables. Decomposition into simple fractions
PC9 Applications of integral (area, length, mean value)
PC10 Improper integral and convergence
PC11 First order linear ODE
PC12 Multivariable real functions. Level curves. Limits and continuity
PC13 Partial derivatives at a point and gradient vector. Linear approximation, tangent plane and differentiability
PC14 Directional derivative. Chain rule. Taylor's polynomials and series
PC15 Quadratic forms and otimization problems

Approval requires a final grade of no less than 10 points (out of 20) in one of the following modalities:
- Continuous Assessment:
* 3 tests (T1, T2, T3), with a minimum score of 7.0 points (out of 20)
* final grade: (T1 + T2 + T3) / 3
* T1 and T2 are taken during the semester
* T3 is taken on the date of the 1st exam period
* T1 or T2 may be retaken on the date of the 1st exam period
or
- Final Exam Assessment (100%).

Oral examinations may be conducted if the instructor deems it appropriate.
Grades above 17 points must be defended in an oral exam.

Mandatory

[1] Stewart, J. (2022). Cálculo, Vol I, Cengage Learning, (9ª Ed.)
[2] Campos Ferreira, J. (2024). Introdução à Análise Matemática, Fundação Calouste Gulbenkian (12ª Ed.)
[3] Lipsman, R.L., Rosenberg, J.M. (2018) Multivariable Calculus with MATLAB, Springer
[4] Hanselman, D., Littlefield, B. and MathWorks Inc. (1997) The Student Edition of MATLAB, 5th Version, Prentice-Hall

Optional

PC1: Introduction to Operating Systems.
PC2: Concurrency and process management.
PC3: Process scheduling and synchronization.
PC4: Interprocess communication.
PC5: Memory management: Models and algorithms.
PC6: Input and output.
PC7: File systems.
PC8: Shell commands and shell programming.
PC9: Virtualization and containerization.

It consists of:

- Two written tests to be taken during the semester with a minimum grade of 8 per test (30% + 30%).

- Practical group work and its oral presentation (40%), with a minimum grade of 8. Group work is mandatory.

Once the practical group work has been completed, if the student does not obtain the minimum grade in the written assessment tests, they may take the theoretical component assessment in an exam worth 60%. If the student obtains exactly 8 in the written component and in order to pass the CU, they must obtain 12 in the practical work, since for the purposes of passing the CU, the student must obtain a grade equal to or higher than 9.5.

Mandatory

- A. S. Tanenbaum and H. Bos, "Modern Operating Systems (4th Ed)", Pearson Prentice-Hall, 2014, ISBN:
978-0133591620
- W. Stallings, "Operating Systems Internals and Principles", 9th Ed, Pearson, 2017, ISBN: 978-0134670959
- M. Portnoy, "Virtualization Essentials", 2nd Ed, 2016, Sybex, ISBN: 978-1119267720
- S. Mohan Jain, "Linux Containers and Virtualization: A Kernel Perspective", Apress, 2020, ISBN: 978-1484262825

Optional

- José Alves Marques, Paulo Ferreira, Carlos Ribeiro, Luís Veiga, Rodrigo Rodrigues, "Sistemas Operativos", FCA, 2012, ISBN 978-972-722-575-0
- Paulo Trezentos e António Cardoso, "Fundamental do Linux", 3ª Edição, FCA, 2009, ISBN: 978-972-722-514-9
- A. Silberschatz, P. Galvin, G. Gagne, "Operating Systems Concepts Essentials", 2nd Ed, Wiley, 2013,
ISBN: 978-1118804926

C1. Fundamentals of Project Management: concepts, characteristics, operations vs. projects
C2. Project Life Cycles and Methodologies (Waterfall, Agile, Hybrid)
C3. Planning and Control Dimensions: scope, time, cost, resources, risk, quality, communication
C4. PM² Methodology: structure, artefacts, stakeholders
C5. Applied Project: planning, execution, monitoring, closure, and benefits evaluation

Assessment is continuous and based on a combination of group and individual evaluation components.
A minimum attendance of two-thirds of the classes is required.
Assessment Components:
1. Project Management Dossier (50%) – Group Assessment
A structured document covering all phases of the project life cycle, based on the PM² methodology, including:
o Project Initiation
o Planning
o Execution and Control
o Closure
2. Final Project Presentation (20%) – Group Assessment
3. Individual Oral Discussion (20%) – Individual Assessment
4. Individual Written Reflection (10%) – Individual Assessment

Mandatory Requirements:
• Minimum grade of 9.5/20 in all assessment components
• Submission of all group project deliverables
This course unit does not include a final exam.

Mandatory

European Commission. (2024). PM² Guide - Open Edition 3.5 (ISBN: 978-92-76-60378-4). Publications Office of the European Union. https://op.europa.eu/pt/publication-detail/-/publication/97cc2f12-c648-11ee-95d9-01aa75ed71a1
Kerzner, H. (2022). Project management: A systems approach to planning, scheduling, and controlling (13th ed.). John Wiley & Sons.
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) (7th ed.). Project Management Institute.
Rigby, D. K., Elk, S., & Berez, S. (2020). Doing agile right: Transformation without chaos. Harvard Business Review Press.
Schwaber, K. (2004). Agile project management with Scrum. Microsoft Press.

Optional

European Commission. (n.d.). PM² Project Management Methodology – Resources. European Commission. https://pm2.europa.eu/pm2-resources_en
European Commission. (n.d.). PM² Essentials: Project Management Methodology. EU Academy. https://academy.europa.eu/courses/pm-c1-pm-essentials-project-management-methodology
European Commission. (n.d.). PM² Agile Essentials. EU Academy. https://academy.europa.eu/courses/pm-a1-pm-agile-essentials
International Organization for Standardization. (2020). ISO 21502:2020—Project, programme and portfolio management: Guidance on project management. ISO.

1. Transformations of work and employment: from the industrial society to the information and knowledge society
2. The challenge of studying work, organisations and technology
3. Rights and duties in the world of work
4. Social theories of technology: impacts, conflicts and assessments
5. Development, innovation and social inequalities
6. The future of work: prospective scenarios and dimensions of analysis
7. Responsibility and ethics in today’s workplace and organisations
8. Precarity in the digital age: concept, forms and practices of precarity
9. Trade unions and companies: participation, representation and collective bargaining
10. Digital transformation in Portugal: advancements and public policies

Assessment throughout the semester:
– Attendance and participation in classes, accounting for 10% of the final mark. A minimum attendance of ⅔ of the classes is required;
– Flipped classroom session, accounting for 20% of the final mark;
– Group assignment, accounting for a total of 35% of the final mark (15% for the written assignment and 20% for the discussion of the assignment);
– Individual written examination, accounting for 35% of the final mark.
Each assessment component has a minimum mark of 8. The final average of the various components must be 9.5 or above.

Exam-based assessment: individual written examination, accounting for 100% of the final mark, with a minimum mark of 9.5.

Mandatory

Agrawal, A., Gans, J, & Goldfarb, A. (2018). Prediction machines. Harvard Business Review Press.
Autor, D. (2022). The labour market impacts of technological change. Working Paper 30074, NBER Working Paper Series.
Benanav, A. (2020). Automation and the future of work. Verso.
Braun, J., Archer, M., Reichberg, G., & Sorondo, M. (2021). Robotics, AI and humanity. Springer.
Crawford, C. (2021). The atlas of AI. Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Edgell, S., & Granter, E. (2020). The sociology of work: Continuity and change in paid and unpaid work. Sage Publications.
Grunwald, A. (2018). Technology assessment in practice and theory. Routledge.
Organização Internacional do Trabalho (2020). As plataformas digitais e o futuro do trabalho. International Labour Office.
Wilkinson, A., & Barry, M. (Eds.) (2021). The future of work and employment. Edward Elgar.
Zuboff, S. (2019). The age of surveillance capitalism. Public Affairs.

Optional

Casilli, A. (2021). Waiting for robots: The ever-elusive myth of automation and the global exploitation of digital labor. Sociologias 57, 112-133.
Cerejeira, J., & Lopes, H. (2023). Economia do trabalho: Mercados e instituições. Edições Almedina.
Estanque, E., Costa, H. A., Fonseca, D., & Santos, A. (2018). Digitalização e precariedade laboral: Novos desafios para o sindicalismo e os movimentos sociais no contexto português. Século XXI, 8(2), 589-621.
Eurofound - European Foundation for the Improvement of Living and Working Conditions (2020). Game-changing technologies: Transforming production and employment in Europe. Publications Office of the European Union.
Hoff, S. S., Langheinrich, M., Hoff, J., Wendehorst, C., Pfeffer, J., Fuchs, T., & Grunwald, A. (2026), The power of 10: New rules for the digital world. Communications of the Association for Computing Machinery, 69(7), 46-59.
International Labour Organisation (2019). Work for a brighter future – Global Commission on the Future of Work. International Labour Office.
Lee, H., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, Article 1121, 1-22.
Organisation for Economic Co-operation and Development (2019). How’s life in the digital age? OECD Publishing.
Standing, G. (2014), The precariat – The new dangerous class. Bloomsbury.
Wajcman, J., & Dodd, N. (2017). The sociology of speed: Digital, organizational, and social temporalities. Oxford University Press.
World Economic Forum (2025). Future of jobs report 2025. World Economic Forum.

S1 User-centred co-creation methodologies based on Design Thinking and Design Sprint
S2 Presentation of case studies and project themes of digital technologies (product, service, or process)
S3 Selection of the project theme, individually or in groups, and its context within the organisation
S4 Problem space: building empathy with the user and their organisation, defining the problem considering business requirements, customer and user needs, and technological challenges
S5 Application of a systematic literature review methodology and its critical analysis. Competitor analysis (if applicable)
S6 Agile project management methodologies suitable for project work
S7 Identification of digital, computational, and other resources (including data collection) necessary for project development
S8 Low and high-fidelity prototyping
S9 Expert evaluation of the usability and task satisfaction of prototypes
S10 Communication of results

Given its eminently practical and applied nature, this curricular unit follows a project-based model — Assessment Throughout the Semester — as provided for in the RGACC, Iscte’s General Regulations for the Assessment of Knowledge and Competences. It does not include assessment by final examination and comprises the following assessment components - 5 project deliverables:
D1 Definition of the project topic – 5%
D2 User empathy, problem definition and user requirements, including personas, usage scenarios, and a prioritised feature backlog - 20%
D3 Review of the literature and existing technological solutions, recommendation of the solution architecture, and planning of the project development – 20%
D4 High-fidelity prototype and expert evaluation – 25%
D5 Presentation, prototype demonstration, and project discussion – 30%

Use of artificial intelligence. The Guidelines for the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026) apply, under the following specific arrangements: the use of artificial intelligence is permitted in project deliverables and must be declared in accordance with Section 4 of the Guidelines, including completion of the attached form; its use is prohibited in tests and practical assessments conducted in class, as well as in any repeat assessment of these components.
For any assessment component, the lecturer or curricular unit coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or assess the knowledge and competences demonstrated by the student.
The assessment includes individual oral verification components in which students are required to explain and modify the work submitted.

To pass the curricular unit, students must achieve a minimum final mark of 10 out of 20, calculated as the weighted sum of all assessment components.

A student will fail this curricular unit if they do not pass the Assessment Throughout the Semester (ALS).

Mandatory

M. Lewrick, P. Link, L. Leifer / Wiley, ISBN 9781119629191, The Design Thinking Toolbox, 2020
J. Knapp, J. Zeratsky, B. Kowitz / Bantam Press., Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days., 2016
A. Osterwalder, Y. Pigneur, P. Papadakos, G. Bernarda, T. Papadakos, A. Smith / John Wiley & Sons., Value proposition design., 2014
T. Brown , H., Collins, 2009, ISBN-13: 978-0062856623, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, 2009
The Basics of User Experience Design by Interaction Design Foundation, https://www.interaction-design.org/

Optional

Snyder, C. (2003). Paper Prototyping: the fast and easy way to design and refine user interfaces. Morgan Kaufmann Publishers.

Nielsen, J. (1994) Enhancing the explanatory power of usability heuristics. Proc. ACM CHI'94 Conf. (Boston, MA, April 24-28), pp. 152-158.

Rettig M. (1994), Prototyping for Tiny Fingers, Communications of The ACM, 1994

CP1. Introduction to computer networks and presentation of the OSI and TCP/IP reference models
CP2. Introduction to the physical and data link layer. Installation and configuration of a switch
CP3. Introduction to the network layer: IPv4 and IPv6 addressing; IPv4 protocol and creating subnets.
CP4. Packet forwarding; Operation and configuration of a router.
CP5. Exploring TCP/UDP transport protocols. Congestion control.
CP6. Exploring the application layer: DNS, E-mail and File Transfer.
CP7. Configuring firewalls and security settings on network infrastructure devices

IThe assessment consists of two components and is conducted as follows:

Theoretical component:
- 1st test to be taken during the middle of the semester (30%);
- 2nd test to be taken during the first exam period (30%).
(There is also the option to take a final exam (60%) for those who did not pass the first or second test)

Practical component:
- One group practical assignment and its presentation (40%), which is mandatory for passing the course.

Note: Both tests and the exam require a minimum grade of 8. The minimum passing grade for the course is 10. The professor reserves the right to administer a supplemental oral exam whenever deemed necessary to clarify questions related to the submitted assessment materials.

Mandatory

-Kurose J., Keith Ross K. (2017). Computer networking: a top-down approach. Pearson. ISBN: 978-0-13-359414-0;
-Tanenbaum A., Wetherall D. (2021). Redes de Computadores. Bookman. ISBN: 9788582605608.

Optional

-Boavida F., Monteiro E. (2021). Engenharia de Redes Informáticas. FCA Editora. ISBN: 9789727226948.

S1 Introduction to mobile application development
S2 Mobile devices features and limitations
S3 Native mobile application development platforms: Google Android, Apple iOS
S4 Web application development for mobile devices with HTML5, CSS3, JS
S5 Hybrid mobile applications development (ionic, ReactNative, Xamarin, Flutter)
S6 Mobile application project planning, design and development

Course with Periodic Assessment, not by Final Exam. Presence is required in 90% of all activities.. Assessment weights:
- Individual practical assignments, 80% of which are compulsory (25%)
- Lab project (in group of 2), with individual oral discussion (50%)
- 2 multiple response Mini-tests (25%)
A mark below 10 assigns the student to an exam in normal and/or the appeal period (50% of the mark), with the completion and approval of the group project, or an individual project is mandatory (50%).

Mandatory

Ramanujam, P., & Natili, G. (2015). PhoneGap: Beginner's Guide. Packt Publishing Ltd. Grummitt, C. (2017). iOS Development with Swift. Manning Publications
Griffith, C. (2017). Mobile App Development with Ionic, Revised Edition: Cross-Platform Apps with Ionic, Angular and Cordova. " O'Reilly Media, Inc.".
Android Programming: The Big Nerd Ranch Guide. Addison-Wesley Professional.
Smyth, N. (2017). Android Studio 3.0 Development Essentials-Android 8 Edition. Payload Media, Inc.. Hardy, B., & Phillips, B. (2013).

Optional

Nahavandipoor, V. (2017). IOS 11 Swift programming cookbook : solutions and examples for iOS apps. O'Reilly.
Keur, C., Hillegass, A. (2016). iOS Programming: The Big Nerd Ranch Guide. Big Nerd Ranch Guides.
New Riders
Welch, S. (2011). From Idea to App: Creating IOS UI, Animations, and Gestures (Voices That Matter).
Collins, C., Galpin, M., & Kaeppler, M. (2011). Android in Practice (p. 648). Manning Publications. Darwin, I. F. (2017). Android Cookbook: Problems and Solutions for Android Developers. " O'Reilly Media, Inc.".
Camden, R., & Matthews, A. (2013). jQuery mobile web development essentials. Packt Publishing Ltd.
SITEPOINT.
Castledine, E., Eftos, M., & Wheeler, M. (2011). Build Mobile: Websites and Apps for Smart Devices.

S1 Introduction to programming language syntax and structure (Python 3)
S2 Integrated Python development environments. Program execution and debugging
S3 Control primitives, variables, expressions and declarations. Objects and object classes.
S4 Functions, modules and packages.
S5 Operations on files. JSON, XML data interpretation.
S6 Database operations. Web scrapping
S7 Introduction to data science. Discussion of problems and case studies. Data cycle and data mining
S8 Types of machine learning
S9 Model the Cross-Industry Standard Process for Data Mining (CRISP-DM)
S10 Data collection and preparation. Evaluating Results
S11 Unsupervised Learning
S12 Supervised Learning
S13 Reinforcement learning

Presence is required in 90% of all the activities of the course. Assessment weights:
- Individual practical work, 8 of which are compulsory (40%).
- Project (in groups of 2), with oral discussion (35%).
- 25% - Mini-tests.
A mark of 10 or higher exempts exam. A mark below assigns the student to an exam in the appeal period.

Mandatory

Larose, D., Larose, C. Data Mining and Predictive Analytics. 2nd Edition, John Wiley & Sons. 2015
Hastie, T.; Tibshirani, R., Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer. 2017
Ethem Alpaydin. Introduction to Machine Learning. MIT Press (2010).ISBN 026201243X.
Kenneth Reitz, Tanya Schlusser, The Hitchhiker's Guide to Python: Best Practices for Development, 1st Edition, 2016, ISBN-13: 978-1491933176, https://docs.python-guide.org/
João P. Martins, Programação em Python: Introdução à programação com múltiplos paradigmas, IST Press, ISBN: 9789898481474. 2015

Optional

John Zelle, Python Programming: An Introduction to Computer Science, Franklin, Beedle & Associates Inc, 2016, ISBN-13 ? : ? 978-1590282755
Eric Matthes, Python Crash Course, 2Nd Edition: A Hands-On, Project-Based Introduction To Programming, No Starch Press,US, 2019, ISBN-13 ? : ? 978-1593279288
David Beazley, Brian Jones, Python Cookbook: Recipes for Mastering Python 3, O'Reilly Media, 2013, ISBN-13 ? : ? 978-1449340377
João Pedro Neto, Programação, Algoritmos e Estruturas de Dados, Escolar Ed., 3ª Edição, 2014. ISBN: 9789725924242

S1 Solution space: User-centred co-creation methodologies applied to ideating the best technological solution for the project
S2 Agile management of the solution phase and project delivery
S3 Tools and techniques for iterative prototyping of the solution
S4 User studies and metrics for evaluating usability and task satisfaction
S5 Data analysis of evaluation results and solution iteration
S6 Solution documentation: backlog of requirements and user stories, design and architecture specifications, hardware and software configuration, installation, operation, and user manuals
S7 Oral, written, and audiovisual communication of results and conference submission with peer review. Elevator pitch for relevant stakeholders
S8 Demonstration in a workshop with relevant stakeholders in the software and applications field

Given its eminently practical and applied nature, this curricular unit follows a project-based model — Assessment Throughout the Semester — as provided for in the RGACC, Iscte’s General Regulations for the Assessment of Knowledge and Competences. It does not include assessment by final examination and comprises the following assessment components: 4 project deliverables:
D1 Solution ideation report, including the storyboard, user journey, final user requirements and backlog, technical specifications, and an audiovisual presentation – 20%
D2 Functional Minimum Viable Product (MVP) on GitHub, and user evaluation report – 30%
D3 Solution design report: conceptual and logical architecture; frontend and backend modules and their respective technologies; database schema; API documentation; hardware and software installation and configuration manual; operations manual; user manual; and recommendations for the continuation of development – 20%
D4 Presentation, MVP demonstration at a workshop, and project discussion – 30%

Use of artificial intelligence. The Guidelines for the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026) apply, under the following specific arrangements: the use of artificial intelligence is permitted in project deliverables and must be declared in accordance with Section 4 of the Guidelines, including completion of the attached form; its use is prohibited in tests and practical assessments conducted in class, as well as in any repeat assessment of these components.
For any assessment component, the lecturer or curricular unit coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or assess the knowledge and competences demonstrated by the student.
The assessment includes individual oral verification components in which students are required to explain and modify the work submitted.

To pass the curricular unit, students must achieve a minimum final mark of 10 out of 20, calculated as the weighted sum of all assessment components.

A student will fail this curricular unit if they do not pass the Assessment Throughout the Semester (ALS).

Mandatory

M. Lewrick, P. Link, L. Leifer / Wiley, ISBN 9781119629191, The Design Thinking Toolbox, 2020
Darrell Rigby, Sarah Elk, Steve Berez / Scrum Institute, The Scrum Framework 3rd Edition
https://www.scrum-institute.org/contents/The_Scrum_Framework_by_International_Scrum_Institute.pdf
Ries, E. / Capítulos 3 e 4, Penguin Group, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses, 2017
J. Knapp, J. Zeratsky, B. Kowitz / Bantam Press., Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days., 2016
A. Osterwalder, Y. Pigneur, P. Papadakos, G. Bernarda, T. Papadakos, A. Smith / John Wiley & Sons., Value proposition design., 2014
T. Brown , H., Collins, 2009, ISBN-13: 978-0062856623, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, 2009
The Basics of User Experience Design by Interaction Design Foundation, https://www.interaction-design.org/

Optional

Doing Agile Right: Transformation Without Chaos Hardcover, 2020
Gwaldis M., How to conduct a successful pilot: Fail fast, safe, and smart, 2019, ·, https://blog.shi.com/melissa-gwaldis/
Jeff Sutherland, J.J. Sutherland, Scrum: The Art of Doing Twice the Work in Half the Time, 2014
Snyder, C. (2003). Paper Prototyping: the fast and easy way to design and refine user interfaces. Morgan Kaufmann Publishers.

Nielsen, J. (1994) Enhancing the explanatory power of usability heuristics. Proc. ACM CHI'94 Conf. (Boston, MA, April 24-28), pp. 152-158.

C1 Introduction to cloud computing. Objectives, distribution models (SaaS, PaaS, IaaS, DBaaS), deployment and infrastructure. Distributed systems? concepts and concurrency. Introduction to cloud security. Redundancy and fault tolerance.
C2 Middleware using web services. Service-oriented architectures. The relationship between SOA and cloud computing. Web, HTTP protocol and RESTful architectural style. Services and communication between services. Technologies and web protocols. Middleware for the cloud.
C3 Distributed processing of large volumes of data. Data organization principles. Brief review on storage models. DaaS and NoSQL. MapReduce programming model.
C4 Developing applications for the Cloud. Integration heterogeneous sources of information. Geographic information distribution, Web Map Service (WMS) and GeoJSON

Course with Periodic Assessment, not by Final Exam. Presence is required in 90% of all the activities.
Assessment weights:
- Individual practical assignments, 80% of which are compulsory (25%)
- Lab project (in group of 2), with individual oral discussion (50%)
- 2 multiple response Mini-tests (25%)
A mark below 10 assigns the student to an exam in normal and/or the appeal period (50% of the mark), with the completion and approval of the group project, or an individual project mandatory (50%).

Mandatory

- Kumar, V. Shindgikar, P. (2018). Modern Big Data Processing with Hadoop. Ed: Packt. ISBN-13: 978-1-78712-276-5
- Etzkorn, Letha (2017). Introduction to Middleware: Web Services, Object Components, and Cloud Computing. Ed: CRC Press. ISBN-13: 978-1-4987-5407-1
- Marinescu, D. (2018). Cloud Computing: Theory and Practice. Ed: Morgan Kaufmann. ISBN-13: 978-0-12-812810-7

Optional

- Chang F., Dean J., Ghemawat S,, C. Hsieh W., Wallach D., Burrows M., Chandra T., Fikes A., and Gruber, R. (2006). Bigtable: a distributed storage system for structured data. In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7 (OSDI '06). USENIX Association, USA, 15.
- Dean J. and Ghemawat S. (2004). MapReduce: simplified data processing on large clusters. In Proceedings of the 6th conference on Symposium on Operating Systems Design & Implementation - Volume 6 (OSDI'04). USENIX Association, USA, 10.
- Ghemawat S., Howard G., and Leung, S. (2003). The Google file system. SIGOPS Oper. Syst. Rev. 37, 5 (December 2003), 29?43. DOI: https://doi.org/10.1145/1165389.945450
- Kumar, V. Shindgikar, P. (2018). Modern Big Data Processing with Hadoop. Ed: Packt. ISBN-13: 978-1-78712-276-5 ? Artigos:
- Etzkorn, Letha (2017). Introduction to Middleware: Web Services, Object Components, and Cloud Computing. Ed: CRC Press. ISBN-13: 978-1-4987-5407-1
- Livros de texto: o Marinescu, D. (2018). Cloud Computing: Theory and Practice. Ed: Morgan Kaufmann. ISBN-13: 978-0-12-812810-7

S1. The digital transformation as a new civilizational paradigm.
S2. The impact of digital technologies on the economy.
S3. The impacts of digital technologies on work.
S4. The impact of digital technologies on inequalities.
S5. The impacts of digital technologies on democracy.
S6. The impacts of digital technologies on art.
S7. The impacts of digital technologies on individual rights.
S8. The impacts of digital technologies on human relations.
S9. The impacts of digital technologies on the future of humanity.
S10. Responsible Artificial Intelligence.
S11. The impact of quantum computing on future technologies.
S12. The impact of digital technologies on geopolitics.

The assessment process includes the following elements:

A) Ongoing assessment throughout the semester
A1. Group debates on issues and problems related to each of the program contents. Each group will participate in three debates throughout the semester. The performance evaluation of each group per debate will account for 15% of each student's final grade within the group, resulting in a total of 3 x 15% = 45% of each student's final grade.
A2. Participation assessment accounting for 5% of each student's final grade.
A3. Final test covering part of the content from the group debates and part from the lectures given by the instructor, representing 50% of each student's final grade.
A minimum score of 9.5 out of 20 is required in each assessment and attendance at a minimum of 3/4 of the classes is mandatory.

B) Final exam assessment Individual written exam, representing 100% of the final grade.

Mandatory

Chalmers, D. (2022). Adventures in technophilosophy In Reality+ - Virtual Worlds and the problems of Philosophy (pp. xi-xviii). W. W. Norton & Company.
Chin, J., Lin, L. (2022). Dystopia on the Doorstep In Deep Utopia – Surveillence State – Inside China’s quest to launch a new era of social control (pp. 5–11). St. Martin’s Press.
Dignum, V. (2019). The ART of AI: Accountability, Responsibility, Transparency In Responsible Artificial Intelligence - How to Develop and Use AI in a Responsible Way (pp. 52–62). Springer.
Howard, P. N. (2020). The Science and Technology of Lie Machines In Lie Machines - How to Save Democracy from Troll Armies, Deceitful Robots, Junk News Operations, and Political Operatives (pp. 1-4; 6-7; 10-18). Yale University Press.
Kearns, M., Roth, A. (2020). Introduction to the Science of Ethical Algorithm Design In The Ethical Algorithm - The Science of Socially Aware Algorithm Design (pp. 1-4; 6-8; 18-21). Oxford University Press.

Optional

(Principal - continuação)
Kissinger, H. A., Schmidt, E., Huttenlocher, D (2021). Security and World Order In The Age of AI - And Our Human Future (pp. 157–167, 173-177). John Murray Publishers.
Parijs, P. V., Vanderborght, Y. (2017). Ethically Justifiable? Free Riding Versus Fair Shares In Basic Income - A Radical Proposal for a Free Society and a Sane Economy (pp. 99–103). Harvard University Press.
Pentland, A. (2014). From Ideas to Actions In Social Physics – How good ideas spread – The lessons from a new science (pp. 4–10). The Penguin Press.
Zuboff, S. (2021). O que é capitalismo de vigilância? In A Era do Capitalismo de Vigilância - A luta por um futuro humano na nova fronteira de poder (pp. 21–25). Intrínseca.

***
(Complementar)
Acemoglu, D.; Johnson, S. (2023). What Is Progress? In Power and progress: our thousand-year struggle over technology and prosperity (pp. 1 - 7). PublicAffairs.
Bostrom, N. (2024). The purpose problem revisited In Deep Utopia – Life and meaning in a solved world (pp. 121–124). Ideapress Publishing.
Castro, P. (2023). O Humanismo Digital do século XXI e a nova Filosofia da Inteligência Artificial In 88 Vozes sobre Inteligência Artificial - O que fica para o homem e o que fica para a máquina? (pp. 563 – 572). Oficina do Livro/ISCTE Executive Education.
Gunkel, D. J. (2012). Introduction to the Machine Question In The Machine Question - Critical Perspectives on AI, Robots, and Ethics (pp. 1-5). The MIT Press.
Innerarity, D. (2023). O sonho da máquina criativa. In Inteligência Artificial e Cultura – Do medo à descoberta (pp. 15 – 26). Colecção Ciência Aberta, Gradiva.
Jonas, H. (1985). Preface to the English version of the Imperative of Responsibility In The Imperative of Responsibility: In Search of an Ethics for the Technological Age. (pp. ix - xii). University of Chicago Press.
Nakazawa, H. (2019). Manifesto of Artificial Intelligence Art and Aesthetics In Artificial Intelligence Art and Aesthetics Exhibition - Archive Collection (p. 25). Artificial Intelligence Art and Aesthetics Research Group (AIAARG).
Patel, N. J. (2022, february 4). Reality or Fiction - Sexual Harassment in VR, The Proteus Effect and the phenomenology of Darth Vader — and other stories. Kabuni. https://medium.com/kabuni/fiction-vs-non-fiction-98aa0098f3b0
Pause Giant AI Experiments: An Open Letter. (22 March, 2023). Future of Life Institute. Obtido 26 de agosto de 2024, de https://futureoflife.org/open-letter/pause-giant-ai-experiments/