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.

S1. Introduction to Machine Learning: The history, foundations and basic concepts
S2. Exploratory Data Analysis (EDA): Data Wrangling and Data Visualization
S3. Classification and Regression; Continuous and categorical / discrete variables; performance evaluation metrics
S4. Supervised Learning: SVM, Decision Trees, Linear and Logistic Regression, Naive-Bayes and k-NN.
S5. Bagging and Boosting in supervised algorithms
S6. Hyperparameter optimization

This curricular unit (CU), due to its highly practical and applied nature, follows a project-based continuous assessment model throughout the semester, and therefore, it does not include a final exam. The assessment consists of 3 assessment blocks (AB), and each AB includes one or more assessment moments. The distribution is as follows:

- AB1: 1st tutorial + 1st mini-test [20% for the 1st tutorial + 10% for the 1st mini-test = 30%]
- AB2: 2nd tutorial + 2nd mini-test [20% for the 2nd tutorial + 10% for the 2nd mini-test = 30%]
- AB3: 3rd tutorial + 3rd mini-test [30% for the 3rd tutorial + 10% for the 3rd mini-test = 40%]

Tutorials consist of projects to be developed in groups, which also include individual oral discussions, allowing for the assessment of students' performance in these same projects.

Mini-tests, taken individually, allow for the assessment of theoretical knowledge applied to each of the projects evaluated in tutorials.

For any assessment component, the lecturer or course coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or to assess the knowledge and skills demonstrated by the student.

The use of generative artificial intelligence tools is permitted solely as a support tool for the learning process; under no circumstances may they replace the student’s intellectual authorship and individual responsibility for any assessment work submitted. The student bears full responsibility for the content submitted and must be able to justify, explain and defend it before the lecturer and/or coordinator. For further clarification, it is essential to read other reference documents relating to the assessment process, such as the Guide to the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026), the Code of Academic Conduct (CCA) and the Student Disciplinary Regulations (RDD).

All periodic assessment blocks (AB1, AB2, and AB3) have a minimum grade of 8.5.

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

To obtain approval for this curricular unit, students must achieve a minimum final mark of 10, calculated as the weighted sum of all assessment components.

A student is considered to have failed this curricular unit if they do not obtain approval in the ALS.

This curricular unit may include the participation of external organizations or companies, which may propose projects that are properly framed within the curriculum and aligned with the learning objectives of the curricular unit.

The 1st and 2nd terms may be used to carry out assessments.

Class attendance is not mandatory.

Mandatory

McMahon, A. (2023). Machine learning engineering with python - second edition: Manage the lifecycle of machine learning models using MLOps with practical examples.
McKinney, W. (2022). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter (3.a ed.). O’Reilly Media.
Burkov, A. (2019). The hundred-page machine learning book. Andriy Burkov.
Mueller, J. P. (2019). Python for Data Science for Dummies, 2nd Edition (2.a ed.). John Wiley & Sons.
VanderPlas, J. (2016). Python Data Science Handbook. O’Reilly Media.

Optional

Ller, A. & Guido, S. (2017). Introduction To Machine Learning with Python: A Guide for Data Scientists. Sebastopol, CA: O'Reilly Media, Inc.
Avila, J. (2017). Scikit-Learn Cookbook - Second Edition. Birmingham: Packt Publishing.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Sharda, R., Delen, D., Turban, E., Aronson, J., & Liang, T. P. (2014). Businesss Intelligence and Analytics: Systems for Decision Support. Prentice Hall.
Foster Provost, Tom Fawcett (2013) Data Science for Business. What you need to know about data mining and data-analytic thinking, 1st edition. O'Reilly.

CP1: Introduction to autonomous agents – properties, models, and applications.
CP2: Agent architectures – reactive, deliberative, hybrid, and knowledge-driven.
CP3: Multi-agent systems – communication, coordination, cooperation, and negotiation.
CP4: Fundamentals of reinforcement learning – models, policies, value functions, and classical algorithms (Q-learning, SARSA).
CP5: Multi-agent learning and simulation environments – competitive and cooperative environments.

Assessment throughout the semester includes:

- A practical project to develop in a team a multi-agent system with learning (maximum of 3 students), including a written report (30%) and an oral defense with an individual grade (20%). Minimum grade of 8 points.
- An individual test (30%) to assess theoretical knowledge.
- Class participation and weekly assignments (20%).

The use of Artificial Intelligence (AI) tools, including generative AI such as ChatGPT, is permitted for technical support and must be explicitly referenced.

The Final Exam is a written, individual, closed-book exam covering all material. Students who did not successfully complete the assessment throughout the semester, with an average grade greater than or equal to 9.5 (out of 20), take the final exam in Época 1, 2, or a special exam..

Mandatory

Russell & Norvig, Artificial Intelligence: A Modern Approach (2022) (capitulos escolhidos)
Sutton & Barto, Reinforcement Learning: An Introduction (2020) (Capitulos escolhidos)

Optional

Disponibilizada no início do semestre com base nos interesses dos estudantes e atualizações recentes.

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. Fundamentals of Graphs and Networks
1.1. Introduction to Graph Theory
1.2. Mathematics of Graphs
1.3. Search Algorithms
PC2. Structure and Dynamics of Complex Networks
2.1. Metrics and Centrality Measures
2.2. Network Models
2.3. Advanced Topics
PC3. Applications and Visualisation of Networks
3.1. Adapting Data to Network Models
3.2. Applications in Various Fields
3.3. Network Visualisation with Computational Tools
PC4. Introduction to Neural Networks
4.1. Perceptron model and basic fundamentals
4.2. Neural network architecture
4.3. Training and optimization

Approval requires a grade of no less than 10 out of 20 in one of the following modalities:
- Assessment throughout the semester: 1 group project (15%) + 1 midterm test (40%) + 1 final test (40% - on the first exam date) + independent work activities (5%); a minimum grade of 8 (out of 20) is required for the midterm test and the final test.
- Assessment by Exam (100%), in any of the exam periods.
- A complementary oral assessment may be conducted after any assessment moment to validate the final grade.

Mandatory

Maarten van Steen, Graph Theory and Complex Networks, 2010, 9081540610,
Mark Newman, Networks, 2018, 978-0198805090,
Sergey N. Dorogovtsev, José F. F. Mendes, The Nature of Complex Networks, 2022
Van Der Hofstad, Remco. Random graphs and complex networks. Vol. 54. Cambridge university press, 2024.
Zhiyuan Liu and Jie Zhou, Introduction to Graph Neural Networks, Morgan & Claypool Publishers. 2020

Optional

Menczer F., Fortunato S., Davis C.A., A first course in network science (Cambridge University Press), 2020, 978-1108471138,
Sayama H., Introduction to the Modeling and Analysis of Complex Systems. Open SUNY Textbooks. Milne Library., 2015
Erwin Kreyszig, Advanced Engineering Mathematics, 2011, 978-0470458365

SY1: Standard W3C and Web programming
SY2: Client-Server architectures
SY3: Model View Controler (MVC)
SY4: Cloud Principles and Business Drivers;
SY5: Pre-Cloud technology, Virtualisation, Hypervisors, Xen, Virtual Clusters;
SY6: XaaS, Public, Private, Hybrid Clouds, Examples;
SY7: Basics on the development of Cloud applications;

This Unit is accomplished through 2 projects that worth 50% each:
(1) Development of a Web Application
(2) Development of a cloud-based project
In the first evaluation period, the project is developed in groups of students with an individual discussion (that accounts for 50% of the grade in each project). As for the second and extra evaluation period, the projects are developed individuallyl.

Mandatory

Buyya, R., Broberg, J, Goscinski, A., "Cloud Computing Principles and Paradigms", Wiley & Sons, 2011
Hwang, K., Fox, G., and Dongarra, J., "Distributed and Cloud Computing (From Parallel Processing to the Internet of Things)", Elsevier, 2011
Vincent W. S. (2018). Build websites with Python and Django. Ed: Independently published. ISBN-10: 1983172669. ISBN-13: 978-1983172663.

Optional

--

S1. Introduction to unsupervised learning: fundamental concepts, types of algorithms, and practical applications.
S2. Dimensionality reduction and data visualization: Principal Component Analysis (PCA), t-SNE, and UMAP for dimensionality reduction and visual interpretation.
S3. Clustering and agglomeration techniques: exploration of algorithms such as K-Means, DBSCAN, and hierarchical clustering.
S4. Analysis and detection of outliers using unsupervised techniques: KNN, LOF, iForest.
S5. SOMs: application of self-organizing maps for visualization and analysis of complex patterns in large volumes of data.
S6. Association rules and Apriori algorithm.
S7. Recommendation systems: general principles, collaborative filtering (user-based and item-based), content-based filtering, and hybrid approaches.
S8. Evaluation and optimization of recommendation systems: metrics (precision, recall, coverage, NDCG, lift) and practical implementation in case studies.

This course unit, due to its highly practical and applied nature, follows the assessment model throughout the semester by project, meaning that this course unit does not have a final exam.
The assessment consists of 3 assessment blocks (AB), and each AB consists of one or more assessment moments. This same structure follows the following distribution:

- AB1: 1st tutorial + 1st mini-test [20% 1st tutorial + 10% 1st mini-test = 30%]
- AB2: 2nd tutorial + 2nd mini-test [20% 2nd tutorial + 10% 2nd mini-test = 30%]
- AB3: 1 final project [40%]

Tutorials consist of group projects, which also include individual oral discussions, allowing for the assessment of student performance in these projects.

Mini-tests, taken individually in class, allow for the assessment of theoretical knowledge applied to each of the projects evaluated in tutorials.

The final project consists of the development of a practical work that brings together the knowledge and skills acquired throughout the semester.

In any AB, an individual oral discussion may be necessary to confirm authorship and/or assess knowledge mastery at any time during the semester.

All periodic assessment blocks (AB1, AB2, and AB3) have a minimum grade of 8.5.

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the CU and/or assignment of 0 points to the assessment element.

This course may include the participation of external organizations or companies, which may propose projects that are properly framed within the curriculum and aligned with the course's learning objectives.

The 1st and 2nd exam periods may be used for assessment.

Class attendance is not mandatory.

Mandatory

1. Berry, M. W., Mohamed, A., & Yap, B. W. (Eds.). (2019). Supervised and unsupervised learning for data science. Springer Nature.
2. Vidal, R., Ma, Y., & Sastry, S. S. (2016). Generalized principal component analysis (Vol. 5). New York: Springer.
3. Reddy, C. K. (2018). Data Clustering: Algorithms and Applications. Chapman and Hall/CRC.
4. McAuley, J. (2022). Personalized machine learning. Cambridge University Press.

Optional

5. Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). Sthda.
6. Verdhan, V. (2020). Models and Algorithms for Unlabelled Data. Springer.
7. Contreras, P., & Murtagh, F. (2015). Hierarchical clustering. In Handbook of cluster analysis (pp. 124-145). Chapman and Hall/CRC.
8. Falk, K. (2019). Practical recommender systems. Manning Publications.
9. Ricci, F., Rokach, L., & Shapira, B. (Eds.). (2022). Recommender systems handbook (3.a ed.). Springer.

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.

S1. AI project-development and planning methodologies.
S2. Presentation of case studies and project topics in digital AI technologies (product, service or process).
S3. Selection of the project theme and its framing within the partner organisation
S4. Problem space: building user and organizational empathy; problem and research-question definition considering business requirements, user/client needs and technological challenges
S5. Application of a systematic literature review methodology and critical analysis of findings
S6. Identification of digital resources (including data collection), computational infrastructure and other assets required for the project.
S7. Preliminary implementation of proposed solutions within the selected case studies/projects; preparation of final reports and documentation; communication of results.

This course unit follows the assessment throughout the semester (ALS) by project, not including a final exam, given the adoption of the teaching method applied to real situations.

The process is based on a single evolutionary report divided into two deliverables:
- RP1: Preliminary Interim Report [40%]
- RP2: Final Preliminary Report [60%]

The Preliminary Interim Report (RP1) includes the project proposal, the framework, a detailed definition of the problem, requirements, and objectives, as well as a literature review and activity plan. It includes an individual oral presentation and discussion.

The Final Preliminary Report (RP2) contains a description of the preliminary implementation, initial results/validation, critical analysis, and the subsequent work plan. It includes an individual oral presentation and discussion.

Periodic meetings, whether in person and/or online, with the mentor from the partner organization and the course instructor/coordinator (or their designee) are mandatory whenever requested.

Each deliverable (RP1 and RP2) requires a minimum grade of 8.5. Attendance at the regular meetings is also taken into account in the evaluation of the deliverables.

For any assessment component, the lecturer or course coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or to assess the knowledge and skills demonstrated by the student.

The use of generative artificial intelligence tools is permitted solely as a support tool for the learning process; under no circumstances may they replace the student’s intellectual authorship and individual responsibility for any assessment work submitted. The student bears full responsibility for the content submitted and must be able to justify, explain and defend it before the lecturer and/or coordinator. For further clarification, it is essential to read other reference documents relating to the assessment process, such as the Guide to the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026), the Code of Academic Conduct (CCA) and the Student Disciplinary Regulations (RDD).

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

To obtain approval for this curricular unit, students must achieve a minimum final mark of 10, calculated as the weighted sum of all assessment components.

A student is considered to have failed this curricular unit if they do not obtain approval in the ALS.

This course unit involves the participation of research units, centers, organizations, internal and external entities, or companies, which propose projects that are appropriately integrated into the curriculum and aligned with the course unit’s learning objectives.

The 1st and 2nd terms may be used to carry out assessments.

Class attendance is not mandatory.

Mandatory

Dooley, J. F., & Kazakova, V. A. (2024). Software development, design, and coding: With patterns, debugging, unit testing, and refactoring. Apress.
Huyen, C. (2022). Designing machine learning systems: An iterative process for production-ready applications (1st ed.). O’Reilly Media.
Ford, N., Richards, M., Sadalage, P., & Dehghani, Z. (2021). Software architecture: The hard parts: Modern tradeoff analysis for distributed architectures. O’Reilly Media.
Lewrick, M., Link, P., & Leifer, L. (2020). The Design Thinking Toolbox: A guide to mastering the most popular and valuable innovation methods (1st ed.). John Wiley & Sons.
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to solve big problems and test new ideas in just five days. Simon & Schuster.
Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2014). Value Proposition Design: How to create products and services customers want. John Wiley & Sons.

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
Darrell Rigby, Sarah Elk, Steve Berez / Scrum Institute, 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/

S1. The concept of Big Data, the applicable problems and the respective ecosystem.
S2. Introduction to non-relational databases and MongoDB.
S3. Computing architecture for Big Data: (1) redundant and fault-tolerant and (2) distributed to support large volumes of data. Example of the Hadoop platform and its distributed file system.
S4. The MapReduce programming model.
S5. Database design in MongoDB.
S6. Handling JSON structures and real-time data.
S7. The ETL (Extract, Transform and Load) process applied to datasets with denormalized real data and the development of Big Data processing applications in Spark and MongoDB environments.

Given the practical and applied nature of this course, the assessment model is followed throughout the semester (ALS) by project and does not include a final exam. The assessment consists of 3 assessment blocks (AB), and each AB consists of one or more assessment moments. It is distributed as follows:

- AB1: 8 weekly assignments [2.5% * 8 = 20% in total]
- AB2: 2 mini-tests [15% each * 2 = 30% in total]
- AB3: Laboratory project [50%]

Each assessment block (AB1, AB2 and AB3) requires a minimum mark of 8.5.

Weekly assignments allow students to respond to small weekly practical challenges related to the knowledge acquired. The mini-tests aim to assess the theoretical knowledge that underlies the syllabus. The laboratory project can be carried out individually or in groups, consisting of the preparation of a practical project which will then be the subject of an individual oral discussion.

For any assessment component, the lecturer or course coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or to assess the knowledge and skills demonstrated by the student.

The use of generative artificial intelligence tools is permitted solely as a support tool for the learning process; under no circumstances may they replace the student’s intellectual authorship and individual responsibility for any assessment work submitted. The student bears full responsibility for the content submitted and must be able to justify, explain and defend it before the lecturer and/or coordinator. For further clarification, it is essential to read other reference documents relating to the assessment process, such as the Guide to the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026), the Code of Academic Conduct (CCA) and the Student Disciplinary Regulations (RDD).

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

To obtain approval for this curricular unit, students must achieve a minimum final mark of 10, calculated as the weighted sum of all assessment components.

A student is considered to have failed this curricular unit if they do not obtain approval in the ALS.

The 1st and 2nd terms may be used to carry out assessments.

Class attendance is not mandatory.

Mandatory

1. Nudurupati, S. (2021). Essential PySpark for Scalable Data Analytics: A beginner’s guide to harnessing the power and ease of PySpark 3. Packt Publishing.
2. Sardar, T. H. (2023). Big data computing: Advances in technologies, methodologies, and applications. CRC Press.
3. Tandon, A., Ryza, S., Laserson, U., Owen, S., & Wills, J. (2022). Advanced analytics with PySpark: Patterns for learning from data at scale using Python and Spark. O’Reilly Media.

Optional

SY1: The utility of large amounts of text, challenges, and current methods.
SY2: Unstructured vs. (semi-)structured information.
SY3: Information retrieval and filtering, information extraction, and Data Mining.
SY4: Document preparation and cleaning, feature extraction, and term weighting strategies.
SY5: Vector space models and similarity measures.
SY6: Introduction to statistical machine learning and evaluation measures.
SY7: Supervised learning: Naïve Bayes, KNN, and k-means.
SY8: Sentiment analysis.
SY9: Resources for Text Mining.
SY10: Introduction to Deep Learning.
SY11: 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]

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.

For any assessment component, the lecturer or course coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or to assess the knowledge and skills demonstrated by the student.

The use of generative artificial intelligence tools is permitted solely as a support tool for the learning process; under no circumstances may they replace the student’s intellectual authorship and individual responsibility for any assessment work submitted. The student bears full responsibility for the content submitted and must be able to justify, explain and defend it before the lecturer and/or coordinator. For further clarification, it is essential to read other reference documents relating to the assessment process, such as the Guide to the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026), the Code of Academic Conduct (CCA) and the Student Disciplinary Regulations (RDD).

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

To obtain approval for this curricular unit, students must achieve a minimum final grade of 10, calculated based on the weighted sum of all assessment components, whether under the ALS model or the Assessment by Exam model.

A student is considered to have failed this curricular unit if they do not pass either the ALS or the Assessment by Exam.

If a student fails the ALS (<10 points), or if the student chooses the Assessment by Exam model, the exam accounts for 100% of the grade.

Class attendance is not mandatory.

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

S1. Strategies with genetic algorithms: evolutionary theory and biological terminology.
S2. Evaluation functions and genetic operators (selection, crossover, mutation). Stopping criteria.
S3. Heuristics and problem representation; search spaces.
S4. Optimization tasks, satisfiability and semi-optimization.
S5. Basic heuristic search strategies: uninformed and informed search.
S6. Advanced heuristic search strategies: heuristics limited by memory and time.

Given the practical and applied nature of this course, the assessment model is followed throughout the semester (ALS) and does not include a final exam. The assessment consists of 2 assessment blocks (AB), and each AB consists of one or more assessment moments. It is distributed as follows:

- AB1: 1st project + 1st mini-test [20% for the 1st project + 15% for the 1st mini-test = 35%]
- AB2: 2nd project + 2nd mini-test [50% for the 2nd project + 15% for the 2nd mini-test = 65%]

Each assessment block (AB1 and AB2) requires a minimum mark of 8.5. In any AB, an individual oral discussion may be required to assess mastery of knowledge at any time during the semester.

The projects consist of deliverables developed in groups and individual oral discussions, allowing the consolidation of content and problem-solving skills to be assessed. The mini-tests aim to gauge the theoretical knowledge underlying the syllabus.

To pass the course, the student must achieve a minimum final mark of 10, resulting from the weighted sum of all the assessment elements.

A student is considered to have failed if they do not pass the ALS.

The 1st Epoch and 2nd Epoch may be used to carry out assessment moments, such as oral discussions and/or project presentations.

Attendance at classes is not compulsory.

Mandatory

1. Artificial Intelligence: A Modern Approach, 4th edition: Stuart Russel and Peter Norvig 2022 Pearson / Prentice Hall.
2. E. Wirsansky, Hands-on genetic algorithms with python. Birmingham, England: Packt Publishing, 2020.
3. An Introduction to Genetic Algorithms, Mitchell M. 1999 MIT Press

Optional

SY1: Review and deepening of project planning and management methodologies in AI, emphasizing co-creation and agile management.
SY2: Presentation of case studies and project themes in digital AI technologies, focusing on the continuation of projects from PAIA-I.
SY3: Selection and refinement of the project theme, considering the organizational context and previously identified challenges.
SY4: Analysis of the problem space: creating empathy with the user and organization, defining and deepening the problem in view of business requirements and technological challenges.
SY5: Continuation of the literature review and critical analysis of the competitive landscape to underpin the proposed solution.
SY6: Identification and integration of the digital, computational, and other resources necessary for project development.
SY7: Development of the applied project, including implementation and improvement of the solution based on feedback, and communication of results.

This course unit adopts the assessment throughout the semester (ALS) model by project, without a final exam, focused on the continuing execution of the project started in the course unit Applied Project in Artificial Intelligence I.

The process is based on a single progress report divided into two deliverables:
- RD1: Interim Final Report [40%] Evaluates the progress of the work
- RD2: Final Report [60%] Presents the consolidated version of the project, integrating the work from both semesters.

The deliverables consist of cumulative documents developed individually or in groups, and individual oral discussions, in public, and together with elements representing the companies / institutions, allowing the development and implementation of the proposed project to be assessed.

Periodic meetings, whether in person and/or online, with the mentor from the partner organization and the course instructor/coordinator (or their designee) are mandatory whenever requested.

Each deliverable (RD1 and RD2) requires a minimum mark of 8.5. Attendance at the regular meetings is also taken into account in the evaluation of the deliverables.

For any assessment component, the lecturer or course coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or to assess the knowledge and skills demonstrated by the student.

The use of generative artificial intelligence tools is permitted solely as a support tool for the learning process; under no circumstances may they replace the student’s intellectual authorship and individual responsibility for any assessment work submitted. The student bears full responsibility for the content submitted and must be able to justify, explain and defend it before the lecturer and/or coordinator. For further clarification, it is essential to read other reference documents relating to the assessment process, such as the Guide to the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026), the Code of Academic Conduct (CCA) and the Student Disciplinary Regulations (RDD).

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

To obtain approval for this curricular unit, students must achieve a minimum final mark of 10, calculated as the weighted sum of all assessment components.

A student is considered to have failed this curricular unit if they do not obtain approval in the ALS.

This course unit involves the participation of research units, centers, organizations, internal and external entities, or companies, which propose projects that are appropriately integrated into the curriculum and aligned with the course unit’s learning objectives.

The 1st and 2nd terms may be used to carry out assessments.

Class attendance is not mandatory.

Mandatory

1. T. Brown / HarperCollins, 2009, ISBN-13: 978-0062856623, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, 2009
2. A. Osterwalder, Y. Pigneur, P. Papadakos, G. Bernarda, T. Papadakos, A. Smith / John Wiley & Sons., Value proposition design, 2014
3. J. Knapp, J. Zeratsky, B. Kowitz, Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days., 2016
4. M. Lewrick, P. Link, L. Leifer / Wiley, ISBN 9781119629191, The Design Thinking Toolbox, 2020
5. Ries, E. / capítulos 3 e 4, Penguin Group, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses, 2017

Optional

6. Scrum Institute, The Kanban Framework 3rd Edition, 2020, www.scrum-institute.org/contents/The_Kanban_Framework_by_International_Scrum_Institute.pdf
7. Darrell Rigby, Sarah Elk, Steve Berez / Scrum Institute, 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
8. Jeff Sutherland, J.J. Sutherland, Scrum: The Art of Doing Twice the Work in Half the Time, 2014
9. Project Management Institute / 6th ed. Newton Square, PA: Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), 2017
10. Gwaldis M., How to conduct a successful pilot: Fail fast, safe, and smart, 2019, ·, https://blog.shi.com/melissa-gwaldis/

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/

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. 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

CP1: Introduction to data analysis – types, sources, and data lifecycle.
CP2: Organizing and cleaning data in Excel – formats, filters, validation. Basic and advanced Excel functions – statistics, logic, search, and reference.
CP3: Basics of Descriptive Statistics: Types of variables. Frequency tables and graphical representations. Measures of central tendency, dispersion, skewness, and kurtosis. Exploratory data analysis.
CP4: Basics of predictive data modeling: Simple and multiple linear regression, logistic regression.
CP5: Data visualization – graphs, conditional formatting, simple dashboards.

Assessment throughout the semester includes:

- Practical data analysis project completed in a group (maximum 3 students), including a report (15%), with mandatory midterm submission (15%) and oral defense with individual grading (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- In-class exercises and weekly 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 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

Wayne L. Winston, Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365), 7th Edition (2021)

Elizabeth Reis, Estatística Descritiva, 7ª Edição, Edições Silabo (2008)

Cole Nussbaumer Knaflic, 'Storytelling with data: a data visualization guide for business professionals', John Wiley & Sons, Inc., 2015.

Optional

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

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.

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. 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

S1. Basic concepts of descriptive statistics, inferential statistics, probabilities and normal distribution
S2. Estimation
S2.1 Confidence intervals for the mean and proportion
S2.2 Determining sample size
S3. Hypothesis tests: parametric and non-parametric tests
S3.1 One-sample t-test
S3.2 t/Mann-Whitney test for two independent samples
S3.3 One-factor ANOVA/Kruskal-Wallis
S3.4 Chi-squared test of independence
S4: Correlation and Regression Line

Pass with a mark of not less than 10 in one of the following ways:
- Assessment throughout the semester: 1 mini-test taken in class (20%) + Final written test taken on the date of the 1st period (60%) + group project (20%),
All assessment elements are compulsory and have a minimum mark of 8.
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

Afonso, A. & Nunes, C. (2019). Probabilidades e Estatística. Aplicações e Soluções em SPSS. Versão revista e aumentada. Universidade de Évora.
Laureano, Raul (2020) - Testes de Hipóteses e Regressão, Lisboa, Edições Sílabo.
Marôco, J. (2018) - Análise Estatística com o SPSS Statistics (7ªed.), Pêro Pinheiro, ReportNumber.
Mehmet, M. and Jakobsen, Tor G. (2022). Applied Statistics Using Stata: A Guide for the Social Sciences (2nd ed.). SAGE Publications.

Optional

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

S1: Fundamental notions of AI with emphasis on the search-based approach.
S2: Search algorithms: depth first and breadth first, A*, greedy BFS, Dijkstra.
S3: Fundamental notions relating to knowledge, representation and the architecture of knowledge-based systems.
S4: First-order predicate logic: representation and deduction.
S5: Declarative knowledge represented in Logic Programming.
S6: Fuzzy logic.

The assessment throughout the semester (ALS) consists of three assessment blocks (AB) and each AB consists of one or more assessment moments. This structure follows the following distribution:

- AB1: 2 practical mini-tests [20% each practical mini-test * 2 = 40%]
- AB2: 2 theoretical mini-tests [15% each theoretical mini-test * 2 = 30%]
- AB3: 1 final project [30%]

Exam assessment:
- 1st Term [100%]
- 2nd Term [100%]

The practical mini-tests, taken individually in class, focus on problem solving, involving the implementation, analysis, and validation of algorithms.

The theoretical mini-tests, also taken individually in class, aim to assess the theoretical knowledge underlying the syllabus content.

The project consists of a group deliverable, including individual oral discussions, which allows for the assessment of content consolidation and problem-solving skills.

For any assessment component, the lecturer or course coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or to assess the knowledge and skills demonstrated by the student.

The use of generative artificial intelligence tools is permitted solely as a support tool for the learning process; under no circumstances may they replace the student’s intellectual authorship and individual responsibility for any assessment work submitted. The student bears full responsibility for the content submitted and must be able to justify, explain and defend it before the lecturer and/or coordinator. For further clarification, it is essential to read other reference documents relating to the assessment process, such as the Guide to the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026), the Code of Academic Conduct (CCA) and the Student Disciplinary Regulations (RDD).

All periodic assessment blocks (AB1, AB2, and AB3) have a minimum grade of 8.5.

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

To obtain approval for this curricular unit, students must achieve a minimum final grade of 10, calculated based on the weighted sum of all assessment components, whether under the ALS model or the Assessment by Exam model.

A student is considered to have failed this curricular unit if they do not pass either the ALS or the Assessment by Exam.

If a student fails the ALS (<10 points), or if the student chooses the Assessment by Exam model, the exam accounts for 100% of the grade.

The exam assessment consists of a written exam covering all the knowledge included in the course unit syllabus.

Class attendance is not mandatory.

Mandatory

Bishara, M. H. A., & Bishara, M. H. A. (2019). Search algorithms types: Breadth and depth first search algorithm
Brachman, R., & Levesque, H. (2004). Knowledge representation and reasoning. Morgan Kaufmann
Clocksin, W. F., & Mellish, C. S. (2003). Programming in Prolog. Springer Berlin Heidelberg.
Russell, S. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.
S., V. C. S., & S., A. H. (2014). Artificial intelligence and machine learning (1.a ed.). PHI Learning.

Optional

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.