PC1. Mathematical Thinking: Algorithmics, Modelling, and Problem Solving
PC2. Mathematical Logic
- Designation, Proposition, and Condition
- Connectives and Semantics
- Quantifiers
- Numerical Systems in Different Bases
PC3. Set Theory
- Definition by Extension and Comprehension, Power Set
- Operations on Sets and Generalisation to Indexed Families
PC4. Proof Methods
- Direct and Indirect Proofs
- Constructive (e.g., Algorithmic Proof) and Non-Constructive Proofs
- Mathematical Induction
PC5. Relations, Partitions, and Recursion
- Cartesian Product and Equivalence and Order Relations
- Partitions and the Equivalence Class Theorem
- Recursive Functions, Sequences, Recurrence Relations, and Summation
PC6. Cardinality, Equipotent Sets, and the Continuum Hypothesis
PC7. Algebraic Structures and Morphisms
PC8. Congruences and Modular Arithmetic
PC9. Divisibility of Integers, Euclidean Algorithm, Prime Numbers, and the Fundamental Theorem of Arithmetic

Pass with a mark of not less than 10 in one of the following ways:
- Assessment throughout the semester: 4 mini-test taken in class (20%) + 4 group works (20%) + Final written test taken on the date of the 1st period (60%); all assessment elements are compulsory and have a minimum mark of 7,0.
or
- Assessment by Exam (100%).

Mandatory

"[1] A Transition to Advanced Mathematics Seventh Edition by Smith, Eggen, St. Andre, Brooks/Cole, Cengage Learning, 2011. [2] Transition to Higher Mathematics: Structure and Proof, Second Edition, Bob A. Dumas, John E. McCarthy, 2014. [3] Lecture Notes for Transition to Advanced Mathematics, James S. Cook, Liberty Universit, 2009."

Optional

PC1 Vectors in R^2 and R^3. Euclidean distance
PC2 Scalar product. Line and parameterization of segments
PC3 Vector product. Orthogonality. Projections. Vector normal to a plane
PC4 Systems of linear equations (SLEs). Gauss-Jordan elimination method
PC5 Matrix writing of SLEs. Algebra of matrices. Transpose of a matrix
PC6 Linear combination of vectors. Linear dependence. Rank of a matrix and Gauss condensation. Rouché's theorem and dependence of variables
PC7 Inverse matrix. Elementary matrices. Permutations and signal. Determinant and propertie
PC8 Minor complementary and adjoint matrix. Laplace's formula
PC9 Inverse matrix method and Cramer's rule in SLEs
PC10 Markov chains and eigenvectors and eigenvalues of a matrix. Characteristic polynomial.
PC10 Fields. R and C
PC11 Real vector spaces. R^2, R^3 and C
PC12 Linear transformations (LTs) and operators. Image and kernel
PC13 Compose of LTs. Geometric changes: uniform expansion and contraction, reflection and rotation

Approval with a classification of no less than 10 points (scale from 0 to 20) in one of the following categories:

- Assessment throughout the semester:
- Test 1 (35%);
- Test 2 (35%), carried out on the date of the 1st season exam;
- Practical group work (20%);
- Exercises solved on Moodle and/or in class (10%).

A minimum rating of 7 values ​​(scale from 0 to 20) is required in each of the tests.

- Assessment by Exam: Individual written test (100%), carried out at any of the exam periods.

The teacher responsible for the curricular unit may decide to hold a complementary oral discussion regarding any planned assessment element, whenever this proves necessary to confirm the authorship of the work carried out, validate 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 nor does it have its own weight in the final classification of the curricular unit. It is intended exclusively for validating the performance demonstrated in the respective assessment element. The classification initially assigned to this element is provisional in nature until the complementary oral discussion is carried out, and may be confirmed or revised depending on the performance demonstrated by the student in the complementary oral discussion and the applicable assessment criteria.

The call for the complementary oral discussion will be communicated to the student at least 24 hours in advance, indicating the date and time of the discussion. Non-attendance, without reasonable justification, makes it impossible to validate the respective assessment element, applying the consequences foreseen for non-completion or withdrawal.

Mandatory

David C. Lay (2015) Linear Algebra and its Applications, Addison Wesley, Pearson
H. Anton and C. Rorres (2010) Elementary Linear Algebra - Applications Version, John Wiley and Sons
Blyth T.S., Robertson E.F. (2002). Basic Linear Algebra. Springer.
Lages E.L. (2015). Geometria Analítica e Álgebra Linear. Coleção Matemática Universitária. IMPA.
The Mathworks, Inc. - The Student Edition of Matlab , Prentice-Hall, 5th Version
Materiais científico-pedagógicos (slides, notas de desenvolvimento, código e pseudo código, fichas de exercícios e problemas) disponibilizados pela equipa docente.

Optional

Cabral I., Perdigão C., Saiago C. (2018). Álgebra Linear Teoria, Exercícios Resolvidos e Exercícios Propostos com Soluções, Escolar Editora.
Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong (2020) Mathematics for Machine Learning, Cambridge University Press [electronic resource: https://mml-book.github.io/book/mml-book.pdf]

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 Real lines and algebra in R. Completeness. Absolute value
PC2 Sequences of real numbers. Recursive definition. Monotonicity. Supreme and infinity. Convergence and framing.
PC3 Notion of numerical series, partial sums and sum. Arithmetic, geometric and harmonic series
PC4 Power series. Convergence
PC5 Functions of R in R. Elementary functions. Parity and transformations to the graph. Period and frequencies.
PC6 Compound and inverse. Asymptotic behavior.
PC7 Logarithm function. Inverse trigonometric. Identities and trigonometric algebra.
PC8 Limits. Continuity. Weierstrass and intermediate value theorems.
PC9 Derivative at a point and its meaning. Mean value theorem. Chain rule and inverse derivative. Implicit derivation
PC10 Taylor approximations. Local and/or global extremes.
PC11 Partitions. Antiderivatives. Riemann definite integral. The fundamental theorem of calculus. Change of variables. Improper integrals. Criteria of integrability

General Criteria:
• Minimum passing grade: 10 out of 20
• Minimum attendance requirement: two-thirds of in-person sessions
• Oral defense is mandatory for final grades ≥17 out of 20
• Two assessment options: “Continuous Assessment” or “Final Exam” (100% exam)

Continuous Assessment (CA):
1. Formative Assessment (40% of final grade)
a) Part-Task Practice – Quizzes or Forums (5%)
* Frequency: weekly, via the online platform
* Immediate feedback and unlimited repetition until mastery
* Success criterion: score ≥16 out of 20 on each task
b) In-Class Learning Tasks (30%)
* Total of 6 tasks (2 per topic: sequences and series, differential calculus, integral calculus)
* Tasks completed during class
* One task per topic may be repeated
* Minimum score to qualify for the summative exam: 8 out of 20
* Each task assesses:
- Mathematical accuracy
- Integration of concepts (graphical, numerical, algebraic)
- Clarity of scientific reasoning
c) Reflective Journal and Independent Work (5%)
* Biweekly entries (2–3 paragraphs) on difficulties and problem-solving strategies
* Assesses: self-regulation and content ownership
2. Pair Project – 10%
a) Task: Model a real-world phenomenon by integrating:
* Power series (LO3, LO7)
* Derivatives and asymptotic analysis (LO6, LO9)
* Definite/improper integrals (LO8, LO9)
b) Format: written report (max. 5 pages) + computational demonstration
3. Summative Assessment (Final Test) – 50%
a) Mixed Format:
* Part A: Part-task practice questions
- Short exercises on limits, derivatives, and basic integrals
* Part B: Integrated learning task
- Contextualized problem
b) Minimum passing grade for the test: 8 out of 20

Assessment by Final Exam Only:
a) Global Exam (100% of final grade)
b) Minimum passing grade: 10 out of 20

Oral Defense:
* Mandatory for students with a final grade ≥17 out of 20

The Course Unit Coordinator may require a supplementary oral discussion in relation to any assessment instrument provided for in this Course Unit Description.
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.
Failure to attend, without a valid justification, shall render validation of the respective assessment instrument impossible, and the consequences applicable to its non-completion or withdrawal shall apply.

Mandatory

Campos Ferreira J. (2024). Introdução à Análise Matemática (12.ª edição). Fundação Calouste Gulbenkian.
Stewart J. (2013). Cálculo. Vol I, 7ª Edição [tradução EZ2 Translate, São Paulo]. Cengage Learning [recurso eletrónico: https://aedmoodle.ufpa.br/pluginfile.php/311602/mod_resource/content/1/Calculo%20-%20James%20Stewart%20-%207%20Edição%20-%20Volume%201.pdf]
Strang, G. (2007). Computational Science and Engineering, Wellesley-Cambridge Press

Optional

Lima E.L. (2001). Análise real. Vol 1. Coleção Matemática Universitária,SBM. Rio de Janeiro.
Ávila G. (2006). Análise Matemática para a Licenciatura. Ed.Edgard Blucher. São Paulo.

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.

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.

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

S1. Introduction to Design Thinking

S2. Stage 1: Empathize

S3. Stage 2: Define the Problem

S4. Stages 3 and 4: Ideate and Prototype

S5. Stage 5: Test and Feedback

S6. Critical Reflection (2 hours)

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

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

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

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

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

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

Mandatory

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

Optional

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

PC1. Topology of Rn. Neighborhood and accumulation point
PC2. Real and vectorial multivariable functions. Level curves and graphical transformations. Directional limits and continuity
PC3. Partial derivatives and gradient vector. Linear approximation and differentiability. Chain rule. Directional derivatives
PC4. Higher order Taylor approximations. Implicit and inverse function theorems and application.
PC5. Hessian matrices and unrestricted extrema. Optimality conditions.
PC6. Exact differential equations.
PC7. Double and triple integrals. Fubini's theorem. Change of coordinates. Polar and spherical coordinates.
PC8. Vector fields and differential forms. Relation between shapes and fields. Properties
PC9. Parameterized curves and surfaces. Tangent and normal vectors. Regularity
PC10. Line and surface integrals. Green's, Stokes' and Gauss' theorems. Conservative field
PC11. Applications of concepts in real context problems

Approval with classification not less than 10 points (1-20 scale) in one of the following modalities:
- Periodic assessment: Test 1 (15%) + Test 2 (15%) + Practical Python work (15%) + 5 online Mini Tests (15%) + Final Test (40%); minimum score of 7 points (1-20 scale) is required on the average Tests 1 and 2, and also in the Final Test
- Assessment by Exam (100%), in any of the exam periods, with individual written test

Mandatory

Stewart, J. (2013). Cálculo. Vol II, 7ª Edição [tradução EZ2 Translate, São Paulo]. Cengage Learning [recurso eletrónico: https://profmcruz.files.wordpress.com/2019/03/calculo-james-stewart-7-edic3a7c3a3o-volume-2.pdf].
Lipsman, R.L., Rosenberg, J.M. (2018). Multivariable Calculus with MATLAB. Springer.
Strang, G. (2007). Computational Science and Engineering, Wellesley-Cambridge Press .
Kong Q., Siauw T., M. Bayen A.M. (2021). Python Programming and Numerical Methods: A Guide for Engineers and Scientists. Elsevier Inc..
Rossun G. (2018). Python Tutorial Release 3.7.0. Python Software Foundation.

Optional

Quarteroni A., Saleri F. (2007). Cálculo Científico com o MATLAB e o Octave. Springer.
Lima E.L. (2000). Curso de Análise, Vol 2, (Projeto Euclides). IMPA.

PC1 Euclidean vector spaces. Orthogonality. Projections. Orthogonal basis. Matrix norms.
PC2 Gram-Schmidt orthogonalization.
PC3 Complex matrices. Eigenvalues and eigenvectors of skew-Hermiteanas. Schur's decomposition. Spectral theorem.
PC4 Linear and bilinear forms. Quadratic forms. Sylvester's theorem. Identification of conics.
PC5 Finite arithmetic. Rounding error. Storing.
PC6 Direct and direct methods for linear systems.
PC7 Consistency, convergence and stability of the numerical methods studied.

Approval with classification not less than 10 points (scale 1-20) in one of the following modalities:
- Assessment throughout the Semester:
* 2 practical works in Python (20% each) with a minimum grade of 7 points
* Test 1 during the semester (30%) with a minimum grade of 7 points
* Test 2 in the date of the first exam (30%) with a minimum grade of 7 points.
or
- Assessment by Exam (100%).

There is the possibility of conducting oral exams.

Mandatory

Ford W. (2015). Numerical Linear Algebra with Applications - using MATLAB. Elsevier
Burden R., Douglas Faires J. (2005). Numerical Analysis. Brooks/Cole Cengage Learning
Gupta R.K. (2019). Numerical Methods: Fundamentals and Applications. Cambridge University Press.
Kong Q., Siauw T., Bayen A.M. (2021). Python Programming and Numerical Methods: A Guide for Engineers and Scientists, Elsevier Inc..
Lay, D.C. (2015). Linear Algebra and its Applications. Addison Wesley. Pearson.
Blyth T.S., Robertson E.F. (2002). Further Linear Algebra. Springer.
Deisenroth M.P., Faisal A.A., Soon Ong C. (2020). Mathematics for Machine Learning. Cambridge University Press [electronic resource: https://mml-book.github.io/book/mml-book.pdf]
Rossun G. (2018). Python Tutorial Release 3.7.0. Python Software Foundation.

Optional

Lima E.L. (2015). Geometria Analítica e Álgebra Linear. Coleção Matemática Universitária, IMPA.
Cabral I., Perdigão C., Saiago C. (2018). Álgebra Linear Teoria, Exercícios Resolvidos e Exercícios Propostos com Soluções (5ª edição). Escolar Editora.
Anton H., Rorres C. (2010). Elementary Linear Algebra - Applications Version. John Wiley and Sons.
Hanselman, D., Littlefield, B. and MathWorks Inc. (1997). The Student Edition of MATLAB, 5th Version, Prentice-Hall

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.

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

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

Mandatory

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

Optional

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

S1: 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

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

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

Optional

PC1. Introduction to Mathematical Modelling (MM)
- MM cycle: formulation, analysis, validation, and interpretation
- Deterministic vs stochastic and continuous vs discrete models
PC2. Ordinary Differential Equations (ODE)
- ODE-based models: populations, radioactive decay, fluid dynamics, epidemiology, harmonic oscillator, and electrical circuits
- First-order ODE: separation of variables, integrating factor, exact and linear equations.
- Second-order ODE: linear equation with constant coefficients
- Systems of ODE
PC3. Introduction to Dynamical Systems (DS)
- Equilibrium points and stability
- Phase diagrams and qualitative analysis of DS
- Attractors, bifurcations, and chaotic behaviour
PC4. MM and Computational Simulation
- Examples of MM in enginnering, physics, biology and economics
- Computational implementation of models
- Simulation of physical phenomena and biological systems
- Model validation: parameter adjustment and error analysis

Approval requires a grade of no less than 10 out of 20 in one of the following modalities:
- Assessment throughout the semester: 1 final test (40% - on the date of the 1st exam period) + 1 midterm test (40%) + autonomous work activities (5%) + computational laboratories (15%); the tests must be at least 8 (out of 20).
- 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

Dennis G. Zill, A First Course in Differential Equations with Modeling Applications - Metric Version, 12th Edition, 2024
William P. Fox, Maurice D. Weir and Frank R. Giordano, A First Course in Mathematical Modeling, 3rd edition, 2003.
William E. Boyce and Richard C. di Prima, Elementary Differential Equations, 10th edition, John Wiley & Sons, 2012.
Martin Braun, Differential Equations and Their Applications: An Introduction to Applied Mathematics, 4th edition, Springer, 1993.

Optional

PC1. Introduction to numerical methods with Python
PC2. Convergence and stability. Error and loss of meaning of numerical approximation
PC3. Numerical derivation. Analytical solution of ordinary differential equations (ODE) of order 1 and 2. Numerical solution of ODE of order 1 (Runge-Kutta method); existence and uniqueness of solutions
PC4. Surface parameterisation and calculation of surface integrals. Spherical coordinates. Numerical integration: Newton-Cotes formulae and Gauss-Legendre quadrature for different numbers of points; graphical interpretation of the quadrature; integration error
PC5. Zeros of a function and finding extremes of functions (with and without differentiability, with and without continuity); bisection and Newton methods, secant method. Introduction to the solution of non-linear systems (Newton's method for systems)
PC6. Difference equations and iterative methods
PC7. Solving higher-order ODEs (Euler finite difference approximations)

Approval with classification not less than 10 points (0-20 scale) in one of the following modalities:
- Assessment Throughout the Semester: one midterm test (25%) + autonomous work exercises submitted in Moodle (5%) + one project with Python in group work (20%) + final individual written test (50%). A minimum score of 7,5 out of 20 is required in each of the midterm test and the final test. Attendance of at least 2/3 of the classes is required. There may be an oral test whenever this clarifies the teacher's opinion of the student's classification.
- Assessment by Exam (100%), in any of the exam periods, with an individual written test.

Mandatory

[1] Kong Q., Siauw T., Bayen A.M. (2021). Python Programming and Numerical Methods: A Guide for Engineers and Scientists. Elsevier Inc..
[2] Gupta R.K. (2019). Numerical Methods: Fundamentals and Applications. Cambridge University Press.
[3] Allen, M.B., Isaacson, E.L. (2019). Numerical analysis for applied science. John Wiley & Sons, Inc..
[4] Rossun G. (2018). Python Tutorial Release 3.7.0. Python Software Foundation.
[5] Christian C. (2017). Differential Equations: A Primer for Scientists and Engineers, Second Edition. Springer International Publishing.

Optional

Cohen H. (2011). Numerical Approximation Methods. Springer New York.

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.

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.

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. 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

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

Optional

P1. Fundamentals of Graphs and Networks
1.1. Introduction to Graph Theory
1.2. Mathematics of Graphs
1.3. Search Algorithms

P2. Structure and Dynamics of Complex Networks
2.1. Metrics and Centrality Measures
2.2. Network Models
2.3. Structure, Stability, and Applications
2.4. Introduction to Neural Networks

P3. 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

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 (40% - presentation on the first exam date) + 1 midterm test (40%) + independent work activities (20%); a minimum grade of 8 (out of 20) is required for the average of the group project and the midterm 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.

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, null,
Erwin Kreyszig, Advanced Engineering Mathematics, 2011, 978-0470458365,

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.

PC1. Polynomials and complex numbers. Elementary complex functions. Laplace equation. Laurent series. Pointwise and uniform convergence. Euler formula.
PC2. Complex variable method for numerical derivation of real functions. Cauchy integral formulas.
PC3. Discrete and continuous signals. Periodicity and generalized function.
PC4. Orthogonality of functions. Fourier series: trigonometric and complex exponential form. Convergence
PC5. Series in solution of differential equations (DEs). Separation of variables. Heat equation. Semi-linear DE. Wave Equation.
PC6. Fourier Transform. Convolution. Partial Black-Scholes Partial DE
PC7. Diagrams (amplitude and phase) of signals in time and frequency domains.
PC8. Discrete Fourier methods. Discrete Fourier Transform: aliasing and the sample theorem.
PC9. Fast Fourier Transform (FFT) and spectral methods. Power spectrum and Parseval's theorem
PC10. Applications to experimental and simulation data

Approval with a classification of no less than 10 points (scale from 0 to 20) in one of the following categories:

- Assessment throughout the semester:
- Test 1 (35%);
- Test 2 (35%), carried out on the date of the 1st season exam;
- Practical group work (20%);
- Exercises solved on Moodle and/or in class (10%).

A minimum rating of 7 values ​​(scale from 0 to 20) is required in each of the tests.

- Assessment by Exam: Individual written test (100%), carried out at any of the exam periods.

The teacher responsible for the curricular unit may decide to hold a complementary oral discussion regarding any planned assessment element, whenever this proves necessary to confirm the authorship of the work carried out, validate 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 nor does it have its own weight in the final classification of the curricular unit. It is intended exclusively for validating the performance demonstrated in the respective assessment element. The classification initially assigned to this element is provisional in nature until the complementary oral discussion is carried out, and may be confirmed or revised depending on the performance demonstrated by the student in the complementary oral discussion and the applicable assessment criteria.

The call for the complementary oral discussion will be communicated to the student at least 24 hours in advance, indicating the date and time of the discussion. Non-attendance, without reasonable justification, makes it impossible to validate the respective assessment element, applying the consequences foreseen for non-completion or withdrawal.

Mandatory

[1] Luis Barreira e Claudia Valls (2021) Equações Diferenciais via Análise Real e Complexa, IST Press
[2] Luis Barreira e Claudia Valls (2021) Exercícios de Equações Diferenciais via Análise Real e Complexa, IST Press
[3] Pedro Girão (2014) Introdução à Análise Complexa, Séries de Fourier e Equações diferenciais, IST Press
[4] Ronald L. Lipsman and Jonathan M. Rosenberg (2018) Multivariable Calculus with MATLAB, Springer
[5] A. V. Oppenheim, A. S. Willlsky (2013) Signals and Systems, 2nd Ed., Pearson
[6] Djairo G. Figueiredo (2022) Análise de Fourier e Equações Diferenciais Parciais. IMPA, 4ª Ed

Optional

PC1. Formulation of optimization problems. Free versus constrained optimization.
PC2. Linear versus nonlinear programming
PC3. Optimality conditions. Limitations of analytical methods
PC4. Concept of convex set and convex function. Convex Optimization.
PC5. Geometric solving techniques
PC6. Linear programming methods. Simplex and big-M
PC7. Duality. Dual problem and Simplex dual algorithm
PC8. Interpretation os solutions and sensitivity analysis
PC9. Discrete optimization fundamentals. Binary, integer and mixed integer programming. Cutting plans. Hybrid Methods.
PC10. Multi-objective linear programming. Goal-oriented programming. Sequential and penalty-weight methods.
PC11. Polynomial approximations and line search methods. Convergence criteria.
PC12. Lagrangean duality. Karush-Kuhn-Tucker conditions.

Approval with classification not less than 10 points (1-20 scale) in one of the following modalities:
- Periodic assessment: Intermediate Test (20%) + 2 Group Work in Python (2x15%) + Autonomous work (10%) + Final Test (40%); minimum score of 7 points (1-20 scale) is required in the Final Test
- Assessment by Exam (100%), in any of the exam periods, with individual written test.

Mandatory

Taha, H.A. (2017). Operations Research: an introduction, 10th Ed.. Pearson.
Ragsdale, C.T. (2017). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. 8th Ed. Cemgage Learning.
Hillier, F.S. Lieberman, G.J. (2014). Introduction to Operations Research, 10th Ed.. McGraw-Hill.
Nash, S.G, Sofer A. (1996). Linear and Nonlinear Programming. McGraw-Hill.

Optional

Winston, W.L. (2004). Operations Research: Applications and Algorithms, 4th Ed.. Duxbury Press.

S1 Co-creation methodologies based on Design Thinking and Design Sprint
C2 Sustainable Development Goals (SDGs) of the United Nations. Creation of value propositions
S3 Presentation of case studies and digital technologies project topics of applied mathematics (product, service or process)
S4 Selecting the project topic and framing it in the organization
S5 Problem space: creating empathy with the user and his organization, defining the problem and its related issues, considering business requirements, customer and user needs, and technology challenges
S6 Application of a systematic literature review methodology and its critical analysis. Competition analysis (if applicable)
S7 Identification of digital resources (including data collection), computational, and other resources required for project development.
S8 Application of agile project management methodologies, appropriate to the group work to be developed by the students of applied mathematics. Communication of results.

Course in periodic assessment, not contemplating final exam, given the adoption of the project-based teaching-learning method applied to real situations. Presentations, demonstrations and discussion will be carried out in groups.
Assessment weights:
R1 Report: Project Topic Definition: 5%.
R2 Report: Empathy with the User and the Organization and Definition of the Problem. Its presentation and group discussion: 40%
R3 Report: Systematic Literature Review and Project Development Planning. Its presentation and group discussion: 55%.

Mandatory

Outra bibliografia dependente dos temas específicos do projeto e das orgaizações onde os alunos o irão desenvolver.
Brown, T (2009), Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, HarperCollins, 2009, ISBN-13: 978-0062856623
Osterwalder, A., Pigneur, Y., Papadakos, P., Bernarda, G., Papadakos, T., & Smith, A. (2014). Value proposition design. John Wiley & Sons.
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days. Bantam Press.
Lewrick, M, Link, P., Leifer, L. (2020). The Design Thinking Toolbox, Wiley, ISBN 9781119629191

Optional

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. Regression models: correlation and causality, simple linear regression, multiple regression. Multicollinearity
PC2. Estimation and inference, OLS (Ordinary Least Squares) and ML (Maximum Likelihood)
PC3. Residual assumptions: hypothesis tests and diagnostics
PC4. Polynomial regression and regression with categorical variables. Dummy variables
PC5. Forecasting (in-sample and out-of-sample). Training and test sets. Metrics for assessing forecasting performance (RMSE-Root Mean Squared Error, MAPE-Mean Absolute Percentage Error, MAE-Mean Absolute Error). Predictive analysis
PC6. Logistic regression. Classification problems. Confidence matrix and ROC (Receiver Operating Characteristic) curve
PC7. Fuzzy sets. QCA (Qualitative Comparative Analysis) on dichotomous variables (csQCA) and on fuzzy sets (fsQCA). Stepwise and temporal QCA (mvQCA and tQCA)
PC8. Other multivariate statistical models: cluster analysis, discriminant analysis and principal components

Pass with a mark of not less than 10 (scale 1-20) in one of the following ways:
- Assessment throughout the semester: a test taken on the date of the 1st term (60%) + practical assignment(s) (30%) + autonomous work (10%).
All assessment elements are compulsory and have a minimum mark of 8.
A complementary oral assessment may be carried out after any assessment moment to validate the final grade.
or
- Assessment by Exam (100%).

Note: 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

Rogel-Salazar J. (2018). Data Science and Analytics with Python. Taylor & Francis Group.
Hastie T.; Tibshirani R.; Friedman J. (2017). The elements of statistical learning: data mining, inference, and prediction. Springer. [electronic resource: https://web.stanford.edu/~hastie/Papers/ESLII.pdf ]
Agresti A.; Franklin C.; Klingenberg B. (2018). Statistics: The Art and Science of Learning from Data, 4th Edition. Pearson.

Optional

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
Albright S.C.; Winston W.L. (2019). Business Analytics: Data Analysis & Decision Making, 7th Edition. Cengage Learning.

PC1. Data collection and preparation: identifying sources, engaging with entities, organising and cleaning data.
PC2. Problem definition and indicators: research question formulation, literature review, identification of relevant metrics.
PC3. Model development and validation: selecting analytical methods, building and testing mathematical or computational models.
PC4. Communication and applicability: writing the final report, preparing the oral presentation, proposing improvements and project reusability.

Assessment comprises three components:

Weekly follow-up (25%) – assesses classroom engagement, attendance, weekly assignments, and project progress.

Final report (50%) – assesses clarity, methodological rigour, and quality of analysis.

Project defence (25%) – assesses the ability to communicate and discuss the work.

Mandatory

Outra bibliografia dependente dos temas específicos do projeto e das organizações onde os alunos o irão desenvolver.

Optional

This CU has the following programmatic contents (PCs):

PC1. Brief review of some concepts of probability theory;
PC2. Introduction to stochastic processes. Different types of stocastic processes: discrete vs continuos descriptions of time and space variable;
PC3. Markov chain: Basic properties;
PC4. Poisson processes;
PC5. Some concepts in measure theory. Wiener processes and Brownian motion;
PC6. Basic theory of stochastic differential equations;
PC7. Numerical methods;
PC8. Study of difussion models in a graph. Applications to complex networks;
PC9. Numerical simulation of a branching process that reveals the dissemination of information in a network.

Approval with an overall grade of at least 10 points (scale 1-20) in one of the following modes:
- Assessment Throughout the Semester: Test 1 (35%) + Test 2 (35%) + practical group work(15%) + exercises solved on Moodle and/or in class (15%). A minimum score of 8 out of 20 is required for each assessment component.
- Exam Assessment: In any exam period, with an individual written exam (100%).

There is the possibility of conducting oral examinations.

Mandatory

1 - Dobrow R.P. (2016). Introduction to Stochastic Processes with R, 1st Edition. Wyley.
2 - Sheldon M. Ross (2024). Introduction to Probability Models, 13th Edition, Academic Press, Elsevier.

Optional

3 - Levin D.A., Peres Y. (2017). Markov Chains and Mixing Times, 2nd Revised edition.American Mathematical Society.
4 - Brzezniak Z., Zastawniak T. (1998). Basic Stochastic Processes: A Course Through Exercises. Springer Undergraduate Mathematics Series.
5 - A. Edelman, "Random matrix theory and its innovative applications" (MATLAB codes) https://math.mit.edu/~edelman/publications/random_matrix_theory_innovative.pdf