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 Epoch [100%]
- 2nd Epoch [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.

In any AB, an individual oral discussion may be required 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.

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

The exam assessment consists of a written exam covering all the knowledge included in the course unit syllabus and has a weighting of 100%.

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

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

Assessment throughout the semester:

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

Assessment by exam:
- One exam, weighted 100%

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

Mandatory

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

Optional

Nunes, O´Neill, Fundamentos de UML, FCA Editora de Informática, 3ª Edição, 2004
C. J. Date, "SQL and Relational Theory: How to Write Accurate SQL Code", 3rd Edition, O'Reilly Media, 2011
Churcher, Clare, “Beginning Database Design: From Novice to Professional”, 2ª edição, Apress. 2012.
Ramakrishnan, R., Gehrke, J. “Database Management Systems”, 3ª edição, McGrawHill, 2003.

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

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

Mandatory

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

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. Advanced Topics

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 date of the 1st exam period) + 2 midterm tests (25% each) + autonomous work activities (10%); all assessment components require a minimum grade of 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

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,

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 and the Laplace transform
- 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) + 2 midterm tests (25% each) + autonomous work activities (10%); the average of the two midterm assessments 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. 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 course unit (CU), due to its highly practical and applied nature, follows a 100% project-based continuous assessment model throughout the semester; 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.

In any AB, there may be a need to conduct an individual oral discussion to assess knowledge.

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.

This CU 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 CU.

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

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 grade not lower than 10 points (scale 1-20) in one of the following modalities:
- Assessment Throughout the Semester:
* 3 group practical assignments (20% each) with a minimum grade of 7 points.
* 2 tests (20% each) with a minimum grade of 7 points.

or

- Examination Assessment (100%).

There is the possibility of oral examination.

Mandatory

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

Optional

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%).

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. 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. Solution space: ideation of the best technological solution relative to the project, development of user requirements, storyboarding, user/costumer journey, iterative prototyping cycles (low fidelity - lo-fi, high fidelity - hi-fi, minimum viable product - MVP), heuristic evaluation of the solution with experts and evaluation with end users.
S2. Production of solution design documentation, including, where applicable, architecture, technical specifications, hardware and software configuration, installation, operation and use manuals.
S3. Experimental deployment of the solution with the potential to be triple sustainable (with economic, social and environmental value creation), safeguarding the applicable legal framework.
S4. Audiovisual communication on the Web and social networks. Communication in public and its structure. Presentation to relevant actors.
S5. Demonstration in workshop with relevant actors in the field of Applied Mathematics.

UC in periodic assessment, not contemplating final exam, given the adoption of the project-based teaching method applied to real situations. Presentations, demonstrations and discussion are carried out in groups.
Evaluation weights:
R1 Solution Ideation Report, with Storyboard, User Journey, User Requirements, Technical Specifications and its audiovisual presentation: 20%.
R2 Solution Prototyping: Lo-fi and Hi-fi Prototypes and Minimum Viable Prototype - MVP (on GitHub), its Demonstration and Evaluation Report: 40%
R3 Solution Design Report with the following elements (if applicable): Architecture (UML Package Diagram, UML Component Diagram), Hardware and Software Configuration, Installation Manual (UML Deployment Diagram, Configuration Tutorial), Operation Manual, User Manual: 20%
R4 Audio-visual presentation of the solution and its demonstration in a Workshop: 20%.

Mandatory

Outra bibliografia dependente dos temas específicos do projeto e das organizações 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
Lewrick, M, Link, P., Leifer, L. (2020). The Design Thinking Toolbox, Wiley, ISBN 9781119629191
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days. Bantam Press.

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 work in Python (15%) + exercises solved on Moodle and/or in class (15%). A minimum score of 7 out of 20 is required for each assessment component.
- Exam Assessment: In any exam period, with an individual written exam (100%).

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

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

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

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

Mandatory

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

Optional

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

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

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

Mandatory

Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C. (2022). Introduction to Algorithms, Fourth Edition. Estados Unidos: MIT Press.
Rocha, A. (2011). Estruturas de Dados e Algoritmos em Java. Portugal: FCA.
Sedgewick, R., Wayne, K. (2014). Algorithms, Part II. Reino Unido: Pearson Education.

Optional

PC1. 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

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

Semester-long Assessment Mode:

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

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

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

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

Mandatory

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

Optional

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

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 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

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

David C. Lay (2015) Linear Algebra and its Applications, Addison Wesley, Pearson
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. 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.

Minimum attendance of no less than 2/3 of classes is required.

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

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

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

Students are assessed based on the following components (A1 + A2):
A1: Learning Tasks with teacher validation (30%)
Five learning tasks will be completed throughout the semester.
The A1 grade corresponds to the average of the grades for the five tasks. To pass A1, the student must meet one of the following requirements:
- obtain at least 7 points in each of the five tasks
or
- obtain a minimum average of 8 points across the five tasks.

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

Remediation:
Students who do not achieve the minimum overall grade may complete an individual Practical Project (100%) with oral discussion.
If a student misses an exam due to absence, or does not achieve the minimum grade of 7 points, they may take a make-up exam at the end of the semester.

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

Mandatory

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

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

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

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

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

Optional

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

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

Kenneth Reitz, Tanya Schlusser, The Hitchhiker's Guide to Python: Best Practices for Development, 1st Edition, 2016, ISBN-13: 978-1491933176, https://docs.python-guide.org/

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

I. Introduction to technological innovation along the lines of Europe
II. Planning a technological project and its phases
III. Essential aspects for the development of a project
IV. Definition of material resources
V. Budget of a project
VI. Partial and joint Test Plan
VII. Presentation of a technological project
VIII. Technological project demonstration
IX. Preparation of Technical Report

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

Mandatory

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

Optional

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

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

Mandatory

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

Optional

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

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

The assessment is aligned with the four components of the 4C/ID model, combining part-task practice, learning tasks, project work, and summative assessment.

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 9 tasks (3 per topic: sequences and series, differential calculus, integral calculus), with the 7 highest scores considered
* 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:
a) Mandatory for students with a final grade ≥17 out of 20
b) Duration: 15 minutes per student
c) Purpose: to confirm understanding of concepts, reasoning, and scientific communication skills

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.

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

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

Individual work accounting for 35% of the final grade.

Group work accounting for a total of 35% of the final grade (10% for the group presentation and 25% for the written work).

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

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

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

Mandatory

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

Optional

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