PC1 - Publicising and selecting projects applied to Integrated Decision Support Systems.
PC2 - Review of the methodology for developing Data Mining projects (CRISP-DM).
PC3 - Survey of the state of the art based on the PRISMA methodology.
PC4 - Project development using techniques and tools appropriate to each project.
PC5 - Templates for publicising the results obtained.
PC6 - Data privacy, access and quality issues.
PC7 - Writing a scientific article.

The course does not have the option of assessment by examination, given its nature as a project applied to real situations.
Assessment throughout the semester is made up of the following assessment components for the practical work/project:
Compulsory continuous assessment (no exam):
3 mid-term deliveries (group, with individual mark): 3 × 5% = 15%

Final report (article format, group, with individual mark): 50%
Presentation and discussion of the project (group, with individual mark): 35%
Additional notes:
Projects can be individual or in groups (max. 4 elements);
Minimum mark of 10 in the final report and presentation;
Mandatory attendance at the final presentation;

Mandatory

Outra bibliografia dependente dos temas específicos do projeto e das orgaizações onde os alunos o irão desenvolver.
T. Brown / HarperCollins, 2009, ISBN-13: 978-0062856623, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, 2009, ·, ·
Value proposition design
J. Knapp, J. Zeratsky, B. Kowitz / Bantam Press, Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days, 2016, ·, ·
M. Lewrick, P. Link, L. Leifer / Wiley, ISBN 9781119629191, The Design Thinking Toolbox, 2020, ·, ·

Optional

Esta UC detém os seguintes conteúdos programáticos (CPs):
CP1. Conceito de logística e de logística na saúde
CP2.Tecnologias digitais aplicadas à logística na saúde e exemplos de “smart hospitals”.
CP3. Qualidade em saúde e o papel da logística digital em saúde
CP4. Desperdício nas organizações de saúde

There are two possible forms of assessment: periodic evaluation and final exams.

Periodic evaluation:
- Elaboration of group work (30%);
- Presentation of group work (20%);
- Participation in class (10%);
- Written test (40%)

The minimum grade for validation of the evaluation in all components is 8 points.

Final exam: individual written test (100%)

Mandatory

M. E. Zonderland, R. J. Boucherie, E. W. Hans & N. Kortbeek (Eds.). / Springer Nature, Handbook of Healthcare Logistics: Bridging the Gap Between Theory and Practice (Vol. 302)., 2021, ·, ·

Optional

Pianykh, O. S., Guitron, S., Parke, D., Zhang, C., Pandharipande, P., Brink, J., & Rosenthal, D. / Nature Machine Intelligence, 2(5), 266-273., Improving healthcare operations management with machine learning, 2020, ·, ·
Prem PrakasH Jayaraman, et al. / Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10.2 e1350., Healthcare 4.0: A review of frontiers in digital health, 2020, ·, ·
T. Dai, S. Tayur / Manufacturing & Service Operations Management, 22(5), 869-887, Om Forum—Healthcare operations management: a snapshot of emerging research, 2020, ·, ·
P. Blua, F. Yalaoui, L. Amodeo, M. De Block, D. Laplanche / John Wiley & Sons, Hospital Logistics and E-management: Digital Transition and Revolution, 2019, ·, ·

This course includes the following programmatic contents (PCs):
PC1: Characterize and elaborate on the concept of a healthcare system (universal or non-universal).
PC2: Establish international comparisons of healthcare systems.
PC3: Understand the concept of health and the dimensions and functions of healthcare systems and their intersectoral nature.
PC4: Map the Portuguese healthcare system (legislation and framing documents of the Portuguese healthcare system).
PC5: Analyze the importance and impact of the digitization of the Portuguese healthcare system.
PC6: Familiarize with the concept of care integration based on the users' journey within the Portuguese healthcare system.
PC7: Deepen the relationship of the Portuguese healthcare system with the citizen users of healthcare services.

Two assessment modalities are planned: 1) Evaluation Throughout the Semester and 2) Exam Assessment.

1) Evaluation Throughout the Semester:

Assessment elements:
Class participation - 20% weight
Group project completion - 20% weight
Group project presentation - 20% weight
Final semester written test - 40% weight

To pass the curricular unit under the Evaluation Throughout the Semester modality, the following are required:
_Minimum attendance of at least two-thirds of the classes
_Minimum grade of 8 in each assessment element
_Final weighted average of the four assessment elements equal to or greater than 9.5

Exam Assessment: Completion of a written exam with a 100% weight and a minimum grade of 9.5.

Mandatory

Beer, M, Eisenstat, R.A.& Spector, B. (2011). Why Change Programs Don’t Produce Change. HBR's 10 Must Reads on Change Management. Harvard Business Review Press.

Boyle, S. (2011). Health Systems in Transition: UK health system review Copenhagen. European Observatory on Health Systems and Policies, in http://www.euro.who.int/__data/assets/pdf_file/0004/135148/e94836.pdf

Campos, L., Borges, M. e Portugal, R. (2009) Governação dos hospitais. Casa das Letras.

Crisp, N. (2015). The Future for Health in Portugal—Everyone Has a Role to Play. Health Systems & Reform, 1(2), 98–106. https://doi.org/10.1080/23288604.2015.1030533

Walshe, K. & Smith, J. (2016). Healthcare Management. Open University Press. McGraw-Hill Education.

Optional

Caldwell, R. (2003), Models of change agency: a fourfold classification, British Journal of Management 14 (2), 131-142.

Cylus, J. (2015). Health Systems in Transition: United Kingdom Health System Review, 17(5). Copenhagen: European Observatory on Health Systems and Policies, in http://www.euro.who.int/__data/assets/pdf_file/0006/302001/UK-HiT.pdf

IOM (1998) To Err is Human. National Academy Press

IOM (2001) Crossing the quality chasm. National Academy Press

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/

PC1. Solution space: ideation of the best technological solution relative to the project, development of user requirements, storyboarding, user/costumer journey, iterative prototyping cycles (low fidelity - lo-fi, high fidelity - hi-fi, minimum viable product - MVP), heuristic evaluation of the solution with experts and evaluation with end users.
PC2. Production of solution design documentation, including, where applicable, architecture, technical specifications, hardware and software configuration, installation, operation and use manuals.
PC3. Experimental deployment of the solution with the potential to be triple sustainable (with economic, social and environmental value creation), safeguarding the applicable legal framework.
PC4. Audiovisual communication on the Web and social networks. Communication in public and its structure. Presentation to relevant actors.
PC5. Demonstration in workshop with relevant actors in the field of digital health technologies.

Assessment throughout the semester is made up of the following assessment components for the practical work/project:
- a mid-term paper presented in class (as a group, with an individual mark) - 10%
- final report in article format (in groups, with an individual mark) - 50%
- presentation and discussion of the project (in groups, with an individual mark): 20%
- poster presentation of the work (20%)
All group members must be present at all presentations.
Projects can be carried out individually or in groups (max. 4 members).
Minimum mark of 10 for the final report and the presentation and discussion of the project.
Project presentations are held with each group during the assessment period.

Mandatory

T. Brown / HarperCollins, 2009, ISBN-13: 978-0062856623, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, 2009.
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.

Optional

Ries, E. / capítulos 3 e 4, Penguin Group, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses, 2017.
Scrum Institute, The Kanban Framework 3rd Edition, 2020, www.scrum-institute.org/contents/The_Kanban_Framework_by_International_Scrum_Institute.pdf acedido em 02/2023
Darrell Rigby, Sarah Elk, Steve Berez / Scrum Institute (2020), The Scrum Framework 3rd Edition, Doing Agile Right: Transformation Without Chaos Hardcover, 2020, www.scrum-institute.org/contents/The_Scrum_Framework_by_International_Scrum_Institute.pdf
Jeff Sutherland, J.J. Sutherland, Scrum: The Art of Doing Twice the Work in Half the Time, 2014,
Project Management Institute / 6th ed. Newton Square, PA: Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), 2017.
Gwaldis M., How to conduct a successful pilot: Fail fast, safe, and smart, 2019, https://blog.shi.com/melissa-gwaldis/ acedido em 02/2023

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.

Chapter 1:
PC1: Introduction to Social Psychology of Health
PC2: The disciplinary field of Social Psychology of Health
PC3: Stress, illness, and coping

Chapter2:
PC4: Health behaviors and illness behaviors
PC5: Explanatory theories of health behaviors
PC6: Quality of life, chronic illness, and hospitalization
PC7: Health promotion and disease prevention

Chapter 3:
PC8: Social Psychology of Health in the technological era
PC9: Technology as a tool in psychological interventions
PC10: Ethics and professional ethics in online interventions

Chapter 4:
PC11: New challenges for intervention

Evaluation throughout the period:

Individual class participation - weighted at 10%, requiring a minimum attendance of at least 2/3 of the classes.
Mid-semester exam - weighted at 25%, minimum grade of 8 out of 20.
Individual pitch at the end of the semester - weighted at 15%, minimum grade of 7.5 out of 20.
Group project, written text, and oral discussion - weighted at 50%, minimum grade of 8.5 out of 20.

The average of these four assessment components must be equal to or greater than 9.5 points.

Evaluation through an exam (available in Period 1 by student choice, Period 2, and Special Period) - weighted at 100%, minimum grade of 9.5 out of 20 or higher.

Mandatory

Baum, A., Revenson, T.A., & Singer, J. (Eds.). (2012). Handbook of Health Psychology (2ª. ed.). NY: Psychology Press.
Friedman, H. S. (Ed.). (2011). The Oxford Handbook of Health Psychology. NY: Oxford University Press.
Ritterband, L., Wessells, D., Ingersoll, K., & Farrell-Carnahan, L. (2019). Technology-Assisted Interventions. In C. Llewellyn, S. Ayers, C. McManus, S. Newman, K. Petrie, T. Revenson, et al. (Eds.), Cambridge Handbook of Psychology, Health and Medicine (Cambridge Handbooks in Psychology, pp. 313-317). Cambridge: Cambridge University Press.
Stephens, C (2008). Health Promotion: A Psychosocial Approach. NY: Open University Press.

Optional

Bartholomew, L. K. (2011). Planning health promotion programs: an intervention mapping approach. San Francisco: Jossey-Bass.
Bull, S. (2010). Technology-based health promotion. Sage.
DiClemente R. J., Crosby R. A., Kegler M. (2009). Emerging Theories in Health Promotion Practice and Research. (2nd edn). San Francisco: Jossey-Bass.
Fertman C. I., Allensworth D. D. (2010). Health Promotion Programs: From Theory to Practice. New York: Society for Public Health Education. San Francisco: Jossey-Bas
Flicker, S., Maley, O., Ridgley, A., Biscope, S., Lombardo, C., & Skinner, H. A. (2008). e-PAR: Using technology and participatory action research to engage youth in health promotion. Action Research, 6(3), 285-303.
Haslam, C, Jetten, J, Cruwys, T, Dingle, G, & Haslam, A (2018). The New Psychology of Health: Unlocking the Social Cure. London: Routledge.
Kato, P.M., & Mann, T. (1996). Handbook of diversity issues in health psychology. New York : Plenum Press.
Marston, H. R., & Hall, A. K. (2016). Gamification: Applications for health promotion and health information technology engagement. In Handbook of research on holistic perspectives in gamification for clinical practice (pp. 78-104). IGI Global.
McKenzie, J.F., Neiger, B.L., Thackeray, R. (2012). Planning, Implementing, & Evaluating Health Promotion Programs: A Primer (6th edition). Benjamin Cummings
Street, R. L., Gold, W. R., & Manning, T. R. (Eds.). (2013). Health promotion and interactive technology: Theoretical applications and future directions. Routledge.
Tse, M. M., Choi, K. C., & Leung, R. S. (2008). E-health for older people: the use of technology in health promotion. CyberPsychology & Behavior, 11(4), 475-479.

This UC has the following contents (CPs):
CP1. Telehealth and Assistive Technologies for health: Concepts, definitions and typology;
CP2. Models of interaction in Telehealth (real time, deferred, etc.);
CP3. General architecture of an IoT system for health: biomedical sensors, acquisition and computing platforms, communication protocols for sensors and actuators, and information systems;
CP4. Conception and design of wearable devices in terms of usability and ergonomics;
CP5. Signal acquisition and processing
CP6. Case studies in telehealth and in assistive technologies for health

Resolution of 2 to 4 exercises/case analysis/individual challenges (R)
2 group assignments (TG1 + TG2) - Groups between 4-6 students.
1 oral presentation/individual written presentation (A)
Final grade = R*0.3 + TG1-2*0.5 + A*0.2
Students who were not successful in the periodic assessment (minimum 10 points) are submitted to a recourse exam worth 100% of the grade

Mandatory

M. von Eiff, W. von Eiff, von Eiff M and von Eiff W (2020) The Digitalisation of Healthcare. HealthManagement.org The Journal, 20(2):182-187, 2020, ·, ·
E. Sazonov / (2nd Edition). Elsevier., Wearable sensors: fundamentals, implementation and applications, 2020, ·, ·
Hariton Costin, Bjorn Schuller, Adina Magda Florea / (Vol. 170). Springer., Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications, 2019, ·, ·
·

Optional

This CU has the following programmatic contents (PCs):
PC1: Introduction to Hospital Data
* Definition and key concepts
PC2: Data Challenges in Healthcare
PC3: AI Foundations for Knowledge Extraction
PC4: AI Techniques for Healthcare Data Analysis
PC5: Ethical and Legal Considerations
PC6: Effective Communication of Findings
PC7: Collaborative AI Projects in Healthcare
PC8: Tools and Libraries for Healthcare Data
PC9: Healthcare Applications of AI
PC10: Project Presentation and Reflection

Assessment will preferably take place continuously throughout the semester (1st Period), consisting of two group assignments, each worth 30% of the final grade, and a written test worth 40%. Each component requires a minimum score of 8 out of 20, and a final weighted average of at least 10 is required to pass the course. A minimum attendance of two-thirds of the classes is also required.
Assignment marks may be adjusted based on individual performance demonstrated during an oral discussion. This may take place if there is a discrepancy greater than 3 points between a student's test score and their assignment score.
Exam Period Assessment (2nd Period and Special)
In the Exam Period, students may opt for an alternative assessment consisting of:
An individual project, worth 60% of the final grade.
A written individual test, worth 40%.
Both components are mandatory and require a minimum score of 8 out of 20. A weighted average of at least 10 is required to pass the course.

Mandatory

McKinney, W. (2017). Python for Data Analysis, 2nd Edition: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media.
Albon, C. (2018). Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning. O'Reilly Media.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media.
Nelson, D. (2020). Data Visualization in Python. Independently Published.
O'Reilly, T. (2010). Open Government: Collaboration, Transparency, and Participation in Practice. O'Reilly Media.
OpenCV. (2024). Open Source Computer Vision Library. Disponível em https://opencv.org/.
TensorFlow. (2024). An Open Source Machine Learning Framework for Everyone. Disponível em https://www.tensorflow.org/.

Optional

S1. Introduction to Information Systems and their role in organisations.
S2. Introduction to Information Systems Analysis with UML: Introduction, requirements analysis, data models, schemas and UML diagrams.
S3. Database Design. Relational Model: relationships, attributes, primary keys, foreign keys, integrity rules, normalisation and optimisations.
S4. SQL Language. Tables, relational algebra, simple queries, subqueries, operators (SELECT, Insert, delete, update), views, indexes, triggers, stored procedures and transactions.
S5. Administration and Security in Database Management Systems (DBMS).

Assessment throughout the semester:

- 3 tests to be taken during the semester (70%). Minimum grade of 8 points for each tests.
- 1 modelling and implementation project (in groups of up to 3 people) (30%). The minimum project grade is 10 points. Completion of the project is mandatory for approval. The project is assessed in groups with individual oral discussion.

Assessment by exam:
- One exam, weighted 100%

The minimum passing grade for the course unit is 10 out of 20.
Attendance at 2/3 of the scheduled classes is mandatory for approval.

Mandatory

Ramos, P, Desenhar Bases de Dados com UML, Conceitos e Exercícios Resolvidos, Editora Sílabo, 2ª Edição, 2007
Elmasri Ramez, Navathe Shamkant, "Fundamentals Of Database Systems", 7th Edition, Pearson, 2016
Damas, L., SQL - Structured Query Language, FCA Editora de Informática, 3ª Edição,2017

Optional

Nunes, O´Neill, Fundamentos de UML, FCA Editora de Informática, 3ª Edição, 2004
C. J. Date, "SQL and Relational Theory: How to Write Accurate SQL Code", 3rd Edition, O'Reilly Media, 2011
Churcher, Clare, “Beginning Database Design: From Novice to Professional”, 2ª edição, Apress. 2012.
Ramakrishnan, R., Gehrke, J. “Database Management Systems”, 3ª edição, McGrawHill, 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

CP1. Management theories
CP2. Management Functions
CP3. Economic features of the Health sector, of healthcare provision and of Health Organizations
CP4. Management functions (planning, organizing, leading and controlling) in Health Organizations

There are two forms of assessment for this course: assessment throughout the semester and final exam.

Evaluation throughout the semester:

Completion/presentation of group assignments (40%);
Mini written tests throughout the semester (60%).
The minimum passing grade for validation of the assessment throughout the semester in each and all components and sub-components is 7.5 points.

Final Exam: Individual written exam (100%).

Mandatory

John Schermerhorn Jr and Daniel Bachrach, Introduction to Management, 2020, Introduction to Management / John Schermerhorn Jr. and Daniel Bachrach (2020, 13th edition) / Wiley, ·

Optional

Economia da saúde : conceitos e comportamentos / Pedro Pita Barros (2019, 4ª ed.) / Almedina
Management: using practice and theory to develop skills / David Boddy (2020, 8th edition) / Pearson;
James A. Johnson & Caren C. Rossow, Health Organizations: Theory, Behavior, and Development, 2017, Health Organizations: Theory, Behavior, and Development / Johnson, J.; Rossow, C. (2017) / Jones & Bartlett Learning, LLC., ·

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.

PC1. Introduction to data mining, decision problems and applications
PC2. The data cycle process.
PC3. Review of descriptive and exploratory statistics.
PC4. Operations with images
PC5. Extraction of image characteristics
PC6. Classical neural networks
PC7. Convolutional neural networks
PC8. Knowledge transfer
PC9. Decision trees.
PC10. Bayesian inference techniques.
PC11. Time series

The preferred mode of assessment is continuous (Exam Period 1), consisting of two group assignments, each worth 30% of the final grade, and one written test worth 40%. Each assessment component requires a minimum mark of 8 out of 20, and students must achieve a final weighted average of at least 10 to pass the course. A minimum attendance of two-thirds of the classes is also required.
Assignment marks may be adjusted based on individual performance demonstrated during an oral discussion. This may occur if there is a discrepancy greater than 3 points between a student's test score and their assignment score.
Exam Period Assessment (2nd Period and Special)
In the Exam Period, students may opt for an alternative assessment consisting of:
An individual project, worth 60% of the final grade.
A written individual test, worth 40% of the final grade.
Both components are mandatory and require a minimum mark of 8 out of 20. A weighted average of at least 10 is required to pass the course.

Mandatory

J. Howse, J. Minichino, Learning OpenCV 4 with Python 3, 3rd Edition, Packt Publishing, 2020.
M. Elgendy, Deep Learning for Vision Systems, Manning, 2020,.
Field Cady - The Data Science Handbook - 1st Edition 2017, Wiley.
Andrw R. Webb, Keith D. Copsey. Statistical Pattern Recognition, 3rd Ed., Wiley, 2011.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer, 2013.
Cathy O'Neil, Rachel Schutt. Doing Data Science: Straight Talk from the Frontline. O'Reilly Media, 2013

Optional

Foster Provost and Tom Fawcett, - Data Science for Business: What you need to know about data mining and data-analytic thinking?, 2013, O'Reilly Media
M. Nixon, A. Aguado, Feature Extraction and Image Processing for Computer Vision, 4th Edition, Academic Press, 2019.
I. Goodsfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016.
OpenCV, https://opencv.org/
Tensorflow, https://www.tensorflow.org/
R. Szeliski, Computer Vision: Algorithms and Applications, 2nd Edition, Springer, 2021, https://szeliski.org/Book/
F. Chollet, Deep Learning with Python, 2nd Edition, Manning, 2021.

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

PC1: Usefulness of large amounts of text, challenges and current methods in healthcare.
PC2: Unstructured vs. (semi-)structured information: Electronic health records, clinical notes and medical examination reports.
PC3: Information retrieval and filtering, information extraction, and Data Mining.
PC4: Document preparation and cleaning, feature extraction, and term weighting strategies.
PC5: Vector space models and similarity measures.
PC6: Introduction to statistical machine learning and evaluation measures.
PC7: Supervised learning: Naïve Bayes, KNN, and k-means.
PC8: Topic Modeling.
PC9: Resources for Text Mining.
PC10: Introduction to Deep Learning.
PC11: LLMs and Retrieval Augmented Generation (RAG) models.

This course follows the semester-long assessment model (ALS).

The ALS consists of the following elements:
- 1 practical work [40%]
- 3 mini-tests [20% each * 3 = 60% in total, with a minimum mark of 8,5]

The practical work can be done individually or in groups, consisting of the development of a project that will be subject to an individual oral discussion.

The practical work has a minimum mark of 9,5.

In case of failure in the ALS (<10 points), or if the student opts for Assessment by Exam, the exam corresponds to 100% of the grade. 40 per cent of the mark corresponds to the project and 60 per cent to the exam mark.

Mandatory

"1. Ozdemir, S. (2023). Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs. Addison-Wesley Professional. 2. Tunstall, L., von Werra, L., & Wolf, T. (2022). Natural language processing with transformers, revised edition. O’Reilly Media. 3. Dan Jurafsky and James H. Martin (Sep 2021). Speech and Language Processing (3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/ 4. Vajjala, S., Majumder, B., Surana, H., & Gupta, A. (2020). Practical natural language processing: A pragmatic approach to processing and analyzing language data. O’Reilly Media. 5. Lane, H., Howard, C., & Hapke, H. (2019). Natural Language Processing in Action (First Edition). Pearson Professional."

Optional

"Charu C. Aggarwal (2018). Machine Learning for Text. https://doi.org/10.1007/978-3-319-73531- 3. Gabe Ignatow, Rada F. Mihalcea (2017). An Introduction to Text Mining: Research Design, Data Collection, and Analysis 1st Edition (2017). SAGE Publications"

"CP1. Introduction to biomedical signals and their importance in healthcare. Practical examples and applications.
CP2. Methods for acquiring and digitizing physiological signals using sensors and devices.
CP3. Fundamentals of biomedical signal analysis with a focus on practical interpretation.
CP4. Pre-processing and filtering techniques for signals using intuitive software.
CP5. Basic concepts of statistics applied to physiological signals with visual interpretation.
CP6. Recording and analysis of brain activity based on typical patterns.
CP7. Cardiac monitoring and electrocardiogram reading through case studies.
CP8. Interpretation of muscle activity from electromyographic signals without advanced calculations.
CP9. Analysis of autonomic responses and electrodermal activity applied to healthcare.
CP10. Introduction to IoT sensors for acquiring and processing physiological signals."

"Continuous Assessment:
a) Practical Project (60%) – Group work with parcial delivery, a technical report and presentation.
b) Written Test (40%) – Theoretical assessment on concepts and methods covered in the course. Minimum grade of 8 out of 20.

Exam Assessment:
a) For students who fail continuous assessment, choose exam assessment, or wish to improve their grade – individual written test with a weighting of 100%, and a minimum grade of 10 out of 20.

The grades for the assignments may vary based on individual performance demonstrated in an oral discussion, which will take place (for the group) if the difference between the test and assignment grades (for any member) exceeds 3 points.

A minimum attendance of at least two-thirds of the classes is required."

Mandatory

"Webster, J. G., & Nimunkar, A. J. (2020). Medical Instrumentation: Application and Design. Wiley. Syed Abdul, S., Luque, L. Fernandez, Garcia-Gomez, J. Miguel, Garcia-Zapirain, B., & Hsueh, P. (2020). Data Analytics and Applications of the Wearable Sensors in Healthcare. Enderle, J. D., & Bronzino, J. D. (2011). Introduction to Biomedical Engineering. Tagawa, T., Tamura, T., & Oberg, P.A. (2011). Biomedical Sensors and Instruments (2nd ed.). "

Optional

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.

Passing with a grade not lower than 10 points in one of the following modalities:
- Assessment throughout the Semester:
* 7 assignments/mini-tests conducted during classes. The best 5 are counted, each with a weight of 5% (total of 25%).
* autonomous work, with a weight of 5%.
* applied mathematics project, with a weight of 10%.
* Final test to be conducted on the date of the first exam period, with a weight of 60% and a minimum grade of 8 points
or
- Examination Assessment (100%).

There is the possibility of conducting oral examinations.
Grades above 17 points must be defended orally.

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.

This UC has the following contents (CPs):
CP1. Elementary notions of anatomy, physiology and pathophysiology
CP2. Semiology and diagnostic algorithm.
CP3. Model diseases (Dementia, COPD, Diabetes Mellitus, Breast Cancer)

There are two possible forms of assessment: periodic evaluation and final exams.
Periodic evaluation:
- Elaboration of group work (30%);
- Presentation of group work (20%);
- Participation in class (10%);
- Written test (40%)

The minimum grade for validation of the evaluation in all components is 8 points.
Final exam: individual written test (100%)

Mandatory

R. Seeley Rod / (6ª ed.). ISBN: 972-8930-07-0, Anatomia & Fisiologia, 2003, ·, ·
Léon Perlemuter, Anatomia e Fisiologia para Cuidados de Enfermagem Lusodidatica (2ª ed), 2003, ·, ·
W. F. Boron, E. L. Boulpaep, Medical Physiology (3rd ed.), 2017, ·, ·
G. M. Cooper / Oxford University Press., The cell: A molecular approach, 2019, ·, ·
A. Kumar, A. K. Abbas, C. Jon, Robbins and Cotran Pathologic Basis of Disease, 2020, ·, ·
S. Guimarães, J. Garrett, W. Osswald, Terapêutica medicamentosa e suas bases farmacológicas: Manual de Farmacologia e Farmacoterapia, 2014, ·, ·
L. L. Brunton / In Knollmann, B. C., & In Hilal-Dandan, R. / (13th ed.). McGraw Hill., Goodman & Gilman's: The Pharmacological Basis of Therapeutics, 2018, ·, ·

Optional

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):
A1: Learning Tasks with teacher validation (30%)
Five learning tasks will be completed throughout the semester.
The A1 grade corresponds to the average of the grades for the five tasks. To pass A1, the student must meet one of the following requirements:
- obtain at least 7 points in each of the five tasks
or
- obtain a minimum average of 8 points across the five tasks.

A2: Mandatory Group Project (3) with theoretical-practical discussion (70%)
Minimum grade of 9.5 points.
Late submissions will result in penalties.

Remediation:
If a student misses an exam due to absence they may take a make-up exam at the end of the semester.

Attendance:
A minimum attendance of 2/3 of classes is required.

Mandatory

Portela, Filipe, Tiago Pereira, Introdução à Algoritmia e Programção com Python, FCA, 2023, ISBN: 9789727229314

Sónia Rolland Sobral, Introdução à Programação Usando Python, 2a ed., Edições Sílabo, 2024, ISBN: 9789895613878

Nilo Ney Coutinho Menezes, Introdução à Programação com Python: Algoritmos e Lógica de Programação Para Iniciantes. Novatec Editora, 2019. ISBN: 978-8575227183

John Zelle, Python Programming: An Introduction to Computer Science, Franklin, Beedle & Associates Inc, 2016, ISBN-13 : ‎978-1590282755

Ernesto Costa, Programação em Python: Fundamentos e Resolução de Problemas, 2015, ISBN 978-972-722-816-4,

Optional

João P. Martins, Programação em Python: Introdução à programação com múltiplos paradigmas, IST Press, 2015, ISBN: 9789898481474

David Beazley, Brian Jones, Python Cookbook: Recipes for Mastering Python 3, O'Reilly Media, 2013, ISBN-13 ? : ? 978-1449340377

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/

Eric Matthes, Python Crash Course, 2Nd Edition: A Hands-On, Project-Based Introduction To Programming, No Starch Press,US, 2019, ISBN-13 ‏: ‎978-1593279288

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.

PC1. Is work different today than it was in the past?
PC2. What rights and duties in the world of work?
PC3. How has theory looked at technology?
PC4. What digital technologies are changing work?
PC5. What future for work?
PC6. Is artificial intelligence really that intelligent?
PC7. Where does precariousness begin and end?
PC8. Who is to blame when the machine makes a mistake?
PC9. Do digital technologies change the relationship between unions and companies?
PC10. What digital transformation in Portugal?

Continuous assessment throughout the semester:
Each student will conduct a Flipped Classroom session, which represents 20% of the final grade.

Individual work accounts for 35% of the final grade.

Completion of a group assignment, worth a total of 35% of the final mark (15% for the written work and 20% for the group discussion).

Attendance and participation in classes represent 10% of the final grade. A minimum attendance of no less than 2/3 of the classes is required.

Each assessment element must have a minimum grade of 8. The final average of the various elements must be equal to or greater than 9.5.

Examination evaluation (1st Period if chosen by the student, 2nd Period, and Special Period): in-person exam representing 100% of the final grade with a minimum grade of 9.5.

Mandatory

Autor, David H., "Why Are There Still So Many Jobs? The History and Future of Workplace Automation.", 2015, Journal of Economic Perspectives, 29 (3): 3-30.
Benanav, A, Automation and the Future of Work, 2020, London: Verso
Boreham, P; Thompson, P; Parker, R; Hall, R, New Technology at Work, 2008, Londres: Routledge.
Crawford, C, The Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence, 2021, Yale University Press.
Edgell, S., Gottfried, H., & Granter, E. (Eds.). (2015). The Sage Handbook of the sociology of work and employment.
Grunwald, A. (2018). Technology Assessment in Practice and Theory. London: Routledge.
Huws, U. (2019) Labour in Contemporary Capitalism, London, Palgrave.
OIT (2020), As plataformas digitais e o futuro do trabalho
Agrawal A, Gans J, Goldfarb A (2018), Prediction Machines, Boston, Massachusetts, Harvard Business Review Press.
Autor D (2022), The labour market impacts of technological change, Working Paper 30074, NBER Working Paper Series.

Optional

✔ Autor D (2022), The labour market impacts of technological change, Working Paper 30074, NBER Working Paper Series.
✔ Braun J, Archer M, Reichberg G, Sorondo M (2021), Robotics, AI and Humanity, Springer.
✔ Cedefop (2022). Setting Europe on course for a human digital transition: new evidence from Cedefop’s second European skills and jobs survey, Publications Office of the European Union.
✔ Eurofound (2020), New forms of employment: 2020 update, Publications Office of the European Union.
✔ ILO (2018), The economics of artificial intelligence: Implications for the future of work, International Labour Office.
✔ ILO (2019), Work for a Brighter Future – Global Commission on the Future of Work. International Labour Office.
✔ Nowotny H (2021), “In AI we trust: how the Covid-19 Pandemic Pushes us Deeper into Digitalization”, Delanty G (ed.) (2021), Pandemics, Politics and Society, De Gruyter, 107-121.
✔ OECD (2019b), How’s Life in the Digital Age?, OECD Publishing.
✔ Wilkinson A, and Barry M (eds) (2021), The Future of Work and Employment, Edward Elgar.
✔ Zuboff S (2019), The Age of Surveillance Capitalism, PublicAffairs.

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

The Semester-Long Assessment includes the following activities:
1. Individual Activities (50%)
1.1 Prompt Simulations with AI Tools in an Academic Context (20%):
* Description: The student must create a clear/justified, well-structured prompt, according to the script proposed by the instructor in class.
* Assessment: (submit in Moodle), communication and teamwork skills based on the quality of the prompt simulations performed.
1.2 Oral Defense - Group Presentation - 5 min. Discussion - 5 min. (30%):
* Description: Each student must present their contributions to the work completed to the class.
* Assessment: After the student's presentation, there will be a question-and-answer session.
2. Group Activities (50%) [students are organized into groups of up to 5 students, randomly selected], which include:
* Group presentations, reviews, edits, and validations of AI-generated content. The assessment (to be submitted in Moodle) includes gathering relevant information, assessing the clarity and innovative nature of the use of structured prompts.
* Development of strategies for reviewing, editing, and validating AI-generated content. Students will be asked to critically evaluate and reflect on the ethical challenges of integrating AI into an academic environment. The assessment (to be submitted in Moodle) will consist of correcting the work based on the accuracy and quality of the reviews and edits, as well as student participation in providing feedback to their peers.
* Final Project Presentation Simulations, where groups choose a topic and create a fictitious project following the structure of a technical report or scientific text. They present their project in class (5 min.) and discuss the topic (5 min.). The assessment (to be submitted in Moodle) will consider the organization, content, correct use of the structure, and procedures of the academic work.

General Considerations:
Feedback on student performance in each activity will be provided during the Semester Assessment.
To be assessed throughout the semester, students must attend 80% of classes and achieve a score of at least 7 points in each assessment.
If there are questions about participation in the activities, the instructor may request an oral discussion.
The group must ensure that at least one computer is available for each group to allow for classroom activities.
There will be no final exam assessment; passing will be determined by the weighted average of the assessments throughout the semester. Assessments in the second and special assessment periods will have an alternative assessment method, so any students wishing to take the assessment in these assessment periods should contact their instructor in advance to learn about the assessment procedure.

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.

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.
Rocha, A. (2011). Estruturas de Dados e Algoritmos em Java. 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

This UC holds the following syllabus (CPs):
CP1. Modeling of business processes in information systems (IS);
CP2. Information Architecture, notions of databases (relational and others) and the use of data extraction tools (ETL);
CP3. Business architecture as a linking tool between healthcare processes and related systems and data;
CP4. Most common information systems in the SNS and non-SNS;
CP5. Security and information segregation in the context of Health,
CP6. Information systems teams in health organizations;
CP7. IS support in the administration function and in the clinical function;
CP8. Models of maturity and digital adoption in healthcare (eg HIMSS);
CP9. Critical Success Factors and Project Implementation Matrix, CRUD and RACI matrix.

Resolution of between 2-4 exercises/case analysis/individual challenges (R)
2 group assignments (TG1 + TG2) - Groups between 4-6 students.
1 oral presentation/individual written presentation (A)
Final grade = R*0.3 + TG1-2*0.5 + A*0.2
Students who were not successful in the periodic assessment (minimum 10 points) are submitted to a recourse exam worth 100% of the grade.

Mandatory

Digital Health: A Framework for Healthcare Transformation White Paper, HIMSS;
Peppard, J., & Ward, J. (2016), The Strategic Management of Information Systems: Building a
Space and Make Competition Irrelevant (1st ed), HBS Press;
Kim, W. C., & Mauborgne, R. (2005), Blue Ocean Strategy: How to Create Uncontested Market
Innovation (1st ed), S.Francisco, CA: Jossey-Bass;
High, P. A. (2014), Implementing World Class IT Strategy: How IT Can Drive Organizational
A. White, Stephen; Miers , Derek (2008) - BPMN Modeling and Reference Guide - Future Strategies Inc;
Rick Sherman (2014) - Business Intelligence Guidebook: From Data Integration to Analytics;
Joey Blue (2014) - What is SQL? Database Learning Basics for Business Professionals, Managers, Accountants, Students, Business Analysts, Bloggers and More?;
Rick Sherman (2014) - Business Intelligence Guidebook: From Data Integration to Analytics;
Simha R. Magal e Jeffrey Word (2009) - Essentials of Business Processes and Information Systems;

Optional

ed), NY: HarperBusiness;
Collins, J. (2001), Good to Great: Why Some Companies Make the Leap and Others Don¿t (1st
Bourgeois, D. T. (2014), Information Systems for Business and Beyond, S.l.: Lulu.com
(2nd ed), FT Management;
Robson, W. (1996), Strategic Management and Information Systems: An Integrated Approach,

S1. Introduction to Design Thinking and Stage 1: Empathy (3h)
S2. Steps 2 and 3: Problem Definition and Ideation (3h)
S3. Step 4: Prototyping (3h)
S4. Step 5: Testing and application of Design Thinking in different areas (3h)

Semester-long Assessment Mode:

• Class participation (20%): Evaluates students' presence, involvement, and contribution in class discussions and activities.

• Individual work (40%): Students will develop an individual project applying Design Thinking to solve a specific problem. They will be evaluated on the application of the stages of Design Thinking, the quality of the proposed solutions, and creativity.

• Group work (40%): Students will form groups to develop a joint project, applying Design Thinking to solve a real challenge. Evaluation will be based on the application of the steps of Design Thinking, the quality of the solutions, and collaboration among group members.

To complete the course in the Semester-long Assessment mode, the student must attend at least 75% of the classes and must not score less than 7 marks in any of the assessment components. The strong focus on learning through practical and project activities means that this course does not include a final assessment mode.

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

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

Lester A. / 7th edition, Elsevier Science & Technology., Project Management Planning and Control, 2017, ·, ·
Tugrul U. Daim, Melinda Pizarro, e outros / Spinger, Planning and Roadmapping Technological Innovations: Cases and Tools (Innovation, Technology, and Knowledge Management), 2014, ·, ·

Optional

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.