CP1: Preliminary data analysis. Exploring libraries for manipulating, cleaning, visualizing, and processing real data, with a focus on preparing data for analysis.
CP2: Variable Analysis and Feature Engineering. Identifying key variables and applying effective selection and transformation techniques to enhance model accuracy and the overall quality of analysis.
CP3: Visualization of univariate and multivariate data.
CP4: Analysis, interpretation, and effective communication of results to support data-driven decision-making.

CP5: Development of an applied project using real-world data, encompassing all stages of the analytical process — from problem definition to the presentation and defense of data-driven solutions

Taking into account the practical nature of the course, assessment is conducted continuously throughout the semester and does not include a final exam. Evaluation is based on two main components:

a) Group quizzes with consultation (40%)
a.1) Two online group quizzes, carried out in class (5% each);
a.2) One comprehensive online group quiz, on the full course content (30%)
b) Group Project (60%)
b.1) Ongoing weekly progress and participation (10%)
b.2) Final written report and oral presentation (50%)
Minimum Requirement: Students must achieve a minimum grade of 9 (out of 20) in each component to pass the course.

Mandatory

(1) Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.
https://link.springer.com/book/10.1007/978-0-387-84858-7

(2) James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). New York: Springer.
https://link.springer.com/book/10.1007/978-1-0716-1418-1

(3) Cady, F. (2024). The data science handbook. John Wiley & Sons.
https://www.wiley.com/en-us/The+Data+Science+Handbook%2C+2nd+Edition-p-9781394234493

(4) Boehmke, B., & Greenwell, B. M. (2019). Hands-on machine learning with R. Chapman and Hall/CRC.
https://bradleyboehmke.github.io/HOML/, https://bradleyboehmke.github.io/HOML/

(5) Wes McKinney (2022), Python for Data Analysis, 3rd Edition, O'Reilly Media, Inc. (https://wesmckinney.com/book/, https://github.com/wesm/pydata-book)

Optional

(1) Harrison, M., & Petrou, T. (2020). Pandas 1. x Cookbook. Packt Publishing.
(2) Mukhiya, S. K., & Ahmed, U. (2020). Hands-On Exploratory Data Analysis with Python: Perform EDA techniques to understand, summarize, and investigate your data. Packt Publishing Ltd.
https://github.com/PacktPublishing/hands-on-exploratory-data-analysis-with-python
(3) Gagolewski, M. (2022). Minimalist Data Wrangling with Python. arXiv preprint arXiv:2211.04630.
https://datawranglingpy.gagolewski.com/

(4) Vasconcelos, J. B., & Barão, A. (2017). Ciência dos dados nas organizações. FCA Editora.

P1. Introduction to Deep Learning
- History, main applications, and case studies
P2. Dense Neural Networks (MLP)
P3. Convolutional Neural Networks (CNNs)
- Fundamentals, popular architectures (LeNet, AlexNet, VGG, ResNet), and applications.
P4. Recurrent Neural Networks (RNNs)
- Fundamentals, the need for LSTMs for long-term dependencies
- Applications in time series forecasting and natural language processing
P5. Implementation and Training of Neural Networks
- Model construction
- Training, validation, and evaluation techniques
- Hyperparameter tuning and regularization techniques
P6. Transformers
- Fundamentals and architecture
- The attention and self-attention mechanism
- BERT and GPT models
- Use of transformers in NLP and other areas such as computer vision.
P7. Generative Models
- Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs)

Assessment throughout the semester: Midterm test (25%) + Project (25%, group work) + Final test (50%, after the end of classes).

Exam-based assessment: Individual test (50%, covering the entire syllabus) + Practical exam (50%, to be carried out on a computer).

The exam-based assessment applies whenever the student opts for this mode or has not obtained a passing grade in the semester assessment. It can be taken in the 1st, 2nd, or special exam periods (Article 14 of the RGACC).

The final Project grade is assigned to each group through a presentation and will depend on the code, the documentation submitted, and the students' performance during the presentation.

Test questions may include aspects related to the project work. None of the assessment components require a minimum grade.

Mandatory

* Deep Learning. Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016 (https://www.deeplearningbook.org/). MIT Press
* Deep learning in Python/ Pytorch, Eli Stevens, Luca Antiga, Thomas Viehmann (2020). Manning Publications (Free book)

Optional

Salganik, M. (2018). Bit by Bit- Social Research in the Digital Age. New Jersey: Princeton University Press Verzani, J. (2014). Using R for Introductory Statistics, 2nd Edition, Chapman & Hall/CRC, eBook ISBN 9781315373089. https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf

S1. Introduction to Longitudinal Data

S2. Time Series
S2.1. Lag operator, stationarity, unit root test
S2.2. White noise, ARMA, ARIMA, SARIMAX models
S2.3. Box-Jenkins methodology, forecasting
S2.4. Applications with R

S3. Panel Data Models
S3.1. Fixed effects models
S3.2. Mixed effects models
S3.3. Growth and change models
S3.4. Generalized linear models
S3.5. Covariance structure in longitudinal data
S3.6. Applications with R

S4. Case Studies

Assessment throughout the semester includes:

a) Group work weighting 40%, with the possibility of discussion if teacher considers it necessary. The minimum grade for the work is 10 points (out of 20). A group presentation should be made in class, and a final report should be handed in, as explained in the Guidelines to the Group Coursework presented to the students.

b) Two individual tests, weighting 20 and 40% of the final mark, the first conducted in October and the second after the end of the classes. A minimum grade of 8.5 points is required in both tests for students to remain in assessment throughout the semester . To approve a weighted average of at least 9.5 points out of 20 is required.

None of this marks can be used in the assessment by exam.



Assessment by exam:

Individual exam that covers the entire syllabus, with a minimum grade of 10 (grade rounded to units).

Both the individual test and the exam will be carried out without consulting support sheets, books or other materials, and the use of graphing calculators or smartphones is not permitted; Students can only consult the form and tables made available in Moodle for this purpose.

Mandatory

Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. Ccom tradução em português disponível em: https://otexts.com/fpppg/.

Baltagi, B. H. (2021) Econometric Analysis of Panel Data, 5th ed. Wiley. (with Student Companion Site)

Optional

Fitzmaurice, G. M., Laird, N. M., Ware, J. H. (2012). Applied Longitudinal Analysis. Wiley .

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.

Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.

S1. Introduction to Simulation in Data Science

S2. Generation of Pseudo-Random Numbers
- Linear Congruential Method; Inverse Transformation Method; Acceptance/Rejection Method; Other Transformations; Mixtures

S3. Monte Carlo Methods in Statistical Inference

S4. Markov Chain Monte Carlo (MCMC) Methods

S5. Resampling Methods
- Bootstrap; Cross-Validation

S6. Discrete Event Simulation

The assessment throughout the semester requires 2/3 of attendance at classes and includes:
1 - Team coursework of 4 to 5 students (30%) with possible individual discussion;
2 - Development of two topics presented during the semester, which will be assessed in the final test;
3 - Final Test (70%).
Approval requires a minimum grade of 8.5 in the final test and a minimum final grade (average) of 10 points.

The assessment may be done through a final exam (100%).

Mandatory

Templ, M. (2016). Simulation for Data Science with R. Packt Publishing Ltd:Birmingham, Uk.
Rizzo, M. L. (2008). Statistical Computing with R. Chapman & Hall/CRC.
Robert, C. P. and Casella, G. (2010). Introducing Monte Carlo Methods with R. Springer-Verlag.

Optional

Wickham, H. and Grolemund , G. (2017). R for Data Science. O'Reilly Media Inc.

CP1 [Introduction]
- The history of the Web;
- Previous and actual programming languages for the web;
- W3C standards;
CP2 [Modelling and programming a Web application]
- Client-server architecture;
- MVC architecture for the Web.
- Main graphical formatting languages for the Web;
- Libraries for graphical formatting;
- Main programming languages for the Web;
- Libraries for programming for the Web;
- Introduction to security on the client and on the server side.
CP3 [Database access]
- Database access from the Web;
- Data model on the Web application and corresponding interaction with the Database.
CP4 [Data Storage and Management]
- Storage of Web data in a Database;
- Data management.

Given the practical nature of the contents, the assessment will encompass a project. Its subject should be aligned with all or part of the syllabus.
Exercises in class (10%).
Project (90%, including teamwork (report and software) 40%, and oral exam 50%).
All components of the project - proposal, report, software, and oral exam, are mandatory. The minimal classification for each component is 10 on a scale of 0 to 20.
There will be a unique deadline for submitting the project, except for students accepted to the special period of assessment, that will be allowed to submit during that period.
Presence in class is not mandatory.
There is no final exam.
Students aiming to improve their classification can submit a new project in the following scholar year.

Mandatory

Mitchell, R. (2016). Web Scraping with Python: Collecting Data from the Modern Web. Ed. O?Reilly Media, Inc. ISBN-13: 978-1491910290. ISBN-10: 1491910291.
Vincent W. S. (2018). Build websites with Python and Django. Ed: Independently published. ISBN-10: 1983172669. ISBN-13: 978-1983172663.
Dean J. (2018). Web Programming with HTML5, CSS, and JavaScript. Ed: Jones & Bartlett Learning. ISBN-13: 978-1284091793. ISBN-10: 1284091791.
Ryan J. (2013). A History of the Internet and the Digital Future. Ed: Reaktion Books. ISBN-13: 978-1780231129

Optional

Lambert M. and Jobsen B. (2017). Complete Bootstrap: Responsive Web Development with Bootstrap 4. Ed: Impackt Publishing. ISBN-10: 1788833406. ISBN-13: 978-1788833400.
Downey A. B. (2015). Think Python: How to Think Like a Computer Scientist. Ed: O'Reilly Media. ISBN-10: 1491939362. ISBN-13: 978-1491939369.

The programme contents (CP) are as follows:
CP1. Introduction to the proposed challenges (projects) and organisation of project teams.
CP2. Information research methodologies for framing the project theme.
CP3. Practical approaches to project development from an ethics-by-design perspective.
CP4. Tools for each stage of project development.
CP5. Usual models for communicating data and results.

Being a project-based course, there is no 100% examination. Assessment runs throughout the semester and consists of the different stages of project development, where:
(i) Each stage is marked by a deliverable (written or presented in class) (E) with feedback.
(ii) An intermediate presentation (A1) with feedback.
(iii) A final presentation (A2) with discussion.
(iv) A poster (informative) (P).
(v) A final project report (R).
The grade will be the result of E x 0.15 + A1 x 0.10 + P x 0.10 + A2 x 0.30 + R x 0.35.

Mandatory

Dependente dos temas específicos do desafio em que cada grupo de estudantes irá desenvolver os trabalhos do projeto.

Optional

Voeneky, S., Kellmeyer, P., Mueller, O., & Burgard, W. (Eds.). 2022. The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives. Cambridge: Cambridge University Press.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Provost, F., & Fawcett, T. 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.

Overview of the Curricular Unit: the need, advantages and disadvantages of essentially symbolic technologies for representing and learning adaptive models of reality, and the role of each programme component in the desiderata of the chair.
Programming in logic to represent models of reality and to reason with them.
Representation and reasoning based on fuzzy sets and in fuzzy logic to represent essentially symbolic adaptive models and reason with them.
Representation and reasoning based on cases to represent essentially symbolic adaptive models and reason with them.
Introduction to Explainable AI and its characteristics and application domains.
Concepts of Responsible AI.

In semester assessment, students will have to take:
- Individual written test on the entire CU programme (60%) - occurring during exams' period (1st or 2nd exam).
- (Group) research work on one of the CU topics, with a report and an oral presentation (40%). The oral presentation is done in class time during the semester. The grade of the research work is split 50% for each item and the members of the group may have different grades.
Both assessment components on semester evaluations have a minimum mark of 8.

Alternatively, students can take only one exam (100%), which can be at both dates of exams.
At special exams' period the students take the exam (100%).

Mandatory

Logic Programming and Inductive Logic Programming:
Ivan Bratko. 2011. Prolog Programming for Artificial Intelligence (4th Edition). Pearson Education Canada (International Computer Science Series).
Fuzzy Systems:
Guanrong Chen, and Trung Tat Pham. 2005. Introduction to Fuzzy Systems. CRC Press.
Case based reasoning:
Michael M. Richter, and Rosina Weber. 2013. Case-Based Reasoning. A Textbook. Springer-Verlag Berlin Heidelberg

Optional

Lynne Billard, Edwin Diday. 2007. Symbolic Data Analysis: Conceptual Statistics and Data Mining, John Wiley & Sons, Ltd, Chichester, UK

1. Introduction to Non Relational Databases;
2. Redundancy as a tool to manage fault tolerance;
3. Distribution of Data to manage large volumes of information;
4. Introduction to MongoDB;
5. Collection Design in MongoDB;
6. Json data structures;
7. Extraction of data in MongoDB;

Assessment throughout the semester is done through a written test (minimum grade 7.5) which takes place on the same date as the 1st season exam and which is worth 70% of the grade and a group work, 30% of the grade ((grade minimum 7.5 values)), to be delivered in the last week of classes. Alternatively, there is assessment by exam. (season 1, season 2 and special season).

Mandatory

NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison, A B M Moniruzzaman, Syed Akhter Hossain, 2013 (https://arxiv.org/abs/1307.0191)
MongoDb Homepage

Optional

Syllabus contents (SC):
SC1- Probability theory: definitions, axioms, conditional probability, total probability theorem and Bayes' formula
SC2- Univariate random variables: mass and density functions, distribution function, and parameters. Working with usual random variables. Simulation of RV with a specified distribution.
SC3-Bi and multivariate RVs. Joint probability and distribution functions. correlation and covariation. Independence between RVs. Sample joint distribution.
SC4- Sampling distributions: limit central theorem, theoretical sampling distributions.
SC5- Parameters estimation: point estimation, estimators' properties, maximum likelihood estimators, interval estimation
SC6- Hypothesis testing: types of errors and corresponding probabilities. Test for one and two means. Chi-square of independence. Meaning and computation of p-values.

Students may choose either Assessment Throughout the Semester or Final Exam.
Assessment Throughout the Semester
1. Small assessment components throughout the semester**: 15% of the final grade. These may include quizzes, homework exercises, contributions to the Glossary or Forum on Moodle, etc. About 10 contributions are expected, with the best 8 being counted.
2. Midterm test, weight: 30%, no minimum grade required.
3. Final test, weight: 55%, minimum grade of 8 out of 20 required.
OR
Final Exam, 100%
A student who, during the regular exam period, has not achieved a passing grade but has scores for #1 (ongoing assessment components throughout the semester) and #2 (midterm test), may use the resit exam period (or special exam period, if applicable) as a replacement for the final test. Their final grade in these periods will be calculated according to the rules of assessment throughout the semester. The student must state this intention in writing on the exam they take.
This option is not available for grade improvement purposes.

Mandatory

Speegle, D., & Clair, B. (2021). Probability, Statistics, and Data: A Fresh Approach Using R (1st ed.). Chapman and Hall/CRC. Free access at https://mathstat.slu.edu/~speegled/_book/
Reis, E., Andrade, M., Calapez, T. & Melo, P., Estatística Aplicada, volume 1. 6ª edição. Lisboa. Edições Sílabo., 2015, ISBN 978-972-618-819-3.
Reis, E., Andrade, M., Calapez, T. & Melo, P., Estatística Aplicada volume 2, 6ª edição, Lisboa. Edições Sílabo., 2016, ISBN 978-972-618-986-2.

Optional

Verzani, J., Using R for Introductory Statistics, 2nd Edition, Chapman & Hall/CRC, 2014, eBook ISBN 9781315373089, https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf
Reis, E., Andrade, M., Calapez, T. & Melo, P., Exercícios de Estatística Aplicada volume 1. 2ª edição, Lisboa. Edições Sílabo., 2012, ISBN 978-972-618-688-5
Reis, E., Andrade, M., Calapez, T. & Melo, P., Exercícios de Estatística Aplicada volume 2. 2ª edição, Lisboa. Edições Sílabo., 2014, ISBN 978-972-618-747-9

P1 - Database Design
P2 Relations and primary keys
P1.2.2 Foreign Keys and Integrity Rules
P1.2.3 Optimizationsand Indexes
P1.2.5 Transctions and Concurrency

P2 S.Q.L
P2. 1 Simpl Querys;
P2.2 Agregate Functions;
P2.3 SubQuerys;
P2.4 Triggers and Stored Procedures;

Assessment is done through exam, sseason 1, season 2 and special season.

Mandatory

-Ramos, P, Desenhar Bases de Dados com UML, Conceitos e Exercícios Resolvidos, Editora Sílabo, 2ª Edição, 2007
-Perreira, J. Tecnologia de Base de Dados" FCA Editora de Informática, 1998
-Damas, L. SQL - Structured Query Language " FCA Editora de Informática, 2005 (II)
http://plsql-tutorial.com/.

Optional

-Date, C.J. "An introduction to Database Systems" Addison-Wesley Publishing Company, sexta edição, 1995 (I.2, I.3, I.4, II);
-Booch, G., Rumbaugh, J., Jacobson, I "The Unified Modeling Language User Guide" Addison-Wesley Publishing Company, 1999 (I.1);
-Nunes, O´Neill, Fundamentos de UML, FCA, 2002

1. Basic Concepts
Elements of a network, subnetworks, density and degree. Bipartite networks.
2. Small Worlds
Degree correlation. Paths and distances. Connectivity. Six Degrees of Separation. Clustering coefficients.
3. Hubs and Weight Heterogeneity
Centrality Measures, Heterogeneity based on Degree, Robustness, Core Decomposition and Weight Heterogeneity.
4. Random Networks
Random Networks generation and characteristics, Watts-Strogatz’s model, Configuration Model, Preferential Models.
5. Communities
Basic Definitions. Related Problems. Methods for community detection (Bridge Removal, Modularity Optimization, Label Propagation) and Evaluation Methods.

Assessment throughout the semester or Assessment by exam.
Assessment throughout the semester:
i) Group coursework:
• Weight of 30% in final grade
• Groups of 4 students
• With oral discussion;
ii) Individual Final Test:
• Weight of 70% in final grade
• Minimum grade required 8.5;
iii) Minimum attendance: 2/3 of classes taught;
iv) Minimum required to approve:
• average ≥ 9.5.
Assessment by exam: 100%
• written exam (100%).
An Oral discussion may be required (both for Assessment throughout the semester and Assessment by exam)
Scale: 0-20 points.

Mandatory

Menczer, F., Fortunato, S. and Davis, C. A. (2020). A First Course in Network Science, 1st edition, Cambridge University Press: Cambridge.
Barabási, A.-L. (2016). Network Science, 1st edition, Cambridge University Press: Cambridge.

Optional

Newman, M. (2018). Networks, 2nd edition. Oxford University Press: Oxford.
https://kateto.net/wp-content/uploads/2018/03/R%20for%20Networks%20Workshop%20-%20Ognyanova%20-%202018.pdf

PC1: Introduction to unsupervised learning methods
PC2: Data reduction techniques (dimensionality)
- Principal components analysis (PCA)
- Data reduction techniques using R
PC3: Clustering techniques
- Hierarchical methods
- Partitioning methods
- Self-organizing maps
- Probabilistic methods
- Quality & Validity of clustering methods
- Clustering techniques using R
PC4: Case studies

Students may choose either Evaluation during the semester or Final Exam.
EVALUATION DURING THE SEMESTER:
- group work with minimum grade 8 (50%)
- individual test with minimum grade 8 (50%)
Approval requires a minimum attendance of 80% of classes and minimum grade of 10.
EXAM:
The Final Exam is a written exam. Students have to achieve a minimum grade of 10 to pass.

Mandatory

Nwanganga, F., M. Chapple (2020), Practical Machine Learning in R, 1st Edition, Wiley. Bouveyron, C., G. Celeux, T. B. Murphy, A. E. Raftery (2019), Model-Based Clustering and Classification for Data Science: With Applications in R, 1st Edition, Cambridge University Press. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013), An Introduction to Statistical Learning: with applications in R, New York: Springer. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2014), Multivariate Data Analysis, 7th Edition, Essex, UK: Pearson Education.

Optional

Wedel, M., Kamakura, W. A. (2000), Market Segmentation. Conceptual and Methodological Foundations (2nd edition), International Series in Quantitative Marketing. Boston: Kluwer Academic Publishers. Lattin, J., D. Carroll e P. Green (2003), Analyzing Multivariate Data, Pacific Grove, CA: Thomson Learning. Kohonen, T. (2001). Self-Organizing Maps. Third edition, Springer. Hennig, C., Meila, M., Murtagh, F., Rocci, R. (eds.) (2016), Handbook of Cluster Analysis, Handbooks of Modern Statistical Methods. Boca Raton: Chapman & Hall/CRC. Aggarwal, C. C., Reddy, C. K. (eds.) (2014), Data Clustering: Algorithms and Applications. Boca Raton: CRC Press.

CP1: Computing platforms for big data
CP2: Machine learning pipeline for big data
CP3: Dimensionality reduction
CP4: Supervised/unsupervised learning for large scale
CP5: Case studies: PageRank and Recommendation Systems

This course includes the following assessment methods: (1) assessment throughout the semester; (2) assessment by exam.

(1) Assessment throughout the semester
The final grade is made up of:
- Individual written test (70%), with a minimum mark of 8.0;
- Group work (30%).
The group work has a mid-term submission, which will count for 30%, and a submission at the end of the semester, which will count for 70%. Those who do not submit the mid-term portion will automatically be assessed by exam.

The work will include an oral presentation/discussion, and the final grade will be individual.

(2) assessment by exam
The final grade will be based on a single written exam, including special season.

Mandatory

- Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale, Ofer Mendelevitch, Casey Stella and Douglas Eadline, Addison-wesley, 2016.
- Advanced Analytics with Spark: Patterns for Learning from Data at Scale, Sandy Ryza et al., O'Reilly Media, 2017.
- Learning Spark: Lightning-Fast Big Data Analysis, Holden Karau, A. Konwinski, P. Wendell and M. Zaharia, O'Reilly Media, 2015.
- Big Data: Algorithms, Analytics, and Applications, Kuan-Ching Li et al., Chapman and Hall/CRC, 2015.
- Mining of Massive Datasets, A. Rajaraman, J. Ullman, 2011, Cambridge University Press.

Optional

- The elements of statistical learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer, 2001
- All of Statistics: A concise course in Statistical Inference, L.Wasserman, Springer, 2003.

Programmatic contents (PC):
PC1. MULTIOBJECTIVE PROGRAMMING
1.1. Basic concepts
1.2. Methodologies
1.2.1. Non-Preemptive Goal Programming
1.2.2. Preemptive Goal Programming

PC2. METAHEURISTICS
2.1. Concepts and terminology
2.2. Single point algorithms
2.2.1. Tabu Search
2.2.2. Simulated Annealing
2.3. Genetic Algorithms

1st SEASON ASSESSMENT
In the 1st Season, the Course can be assessed using the Assessment throughout the Semester or by completing an Individual Project.

--> ASSESSMENT THROUGHOUT THE SEMESTER
- Individual Assignment (30%): minimum mark of 8;
- Group Project (70%): written report and code (45%) + oral presentation (10%) + individual test (15%).

Conditions associated with assessment throughout the semester:
(i) Maximum number of students who can make up a working group: 5;
(ii) The student must participate in all moments of assessment throughout the semester.

--> EVALUATION THROUGH THE REALIZATION OF AN INDIVIDUAL PROJECT
- Written report and code (75%)
- Oral discussion (25%).
The student must participate in both parts of the individual assessment.


2nd SEASON ASSESSMENT
In the 2nd Season, the Course is assessed through the completion of an Individual Project.

--> EVALUATION THROUGH THE REALIZATION OF AN INDIVIDUAL PROJECT
- Written report and code (75%)
- Oral discussion (25%).
The student must participate in both parts of the individual assessment.

In both seasons, an oral exam may be required even if final grade >= 9,5.

Scale 0-20

Mandatory

- Ke-Lin Du; M. N. S. Swamy (2018). Search and Optimization by Metaheuristics: Techniques and and Algorithms Inspired by Nature. Birkhäuser.
- Gutierrez, A. M; Ramirez-Mendoza, R. A.; Flores, E. M.; Ponce-Cruz, P; Espinoza, A.A. O.; Silva, D. C. B. (Eds.) (2020). A Practical Approach to Metaheuristics using LabVIEW and MATLAB (R). Taylor & Francis Ltd.
- Lobato, F. S.; Valder, S. Jr. (2017). Multi-Objective Optimization Problems: Concepts and Self-Adaptive Parameters with Mathematical and Engineering Applications. Springer Cham.
- Ragsdale, C.T. (2017). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. 8th Ed. Cemgage Learning.
- Burke, E. K.; Kendall, G. (Eds.) (2014). Search Methodologies: Introductory Tutorials in Optimization and Decision Support, 2nd edition, Springer.
- Siarry, P. (Ed.) (2016). Metaheuristics, Springer.
- Ehrgott, M. (2005). Multicriteria Optimization, 2nd edition, Springer.

Optional

- Open Access documents such as instructor notes, book chapters, research articles, and tutorials that will be provided via Moodle.

S1. Regression models
S1.1. Correlation
S1.2. Simple linear regression
S1.3. Multiple linear regression
S2. Estimation and inference, OLS and ML
S3. Residual assumptions
S3.1. Diagnostic and Hypothesis tests
S3.2. Practical cases
S4. Binary choice Regression Models S4.1 Linear Probability Model
S4,2 Tobit and Probit Models
S5. Extensions of the classical regression model
S5.1. Quantile Regression
S5.2. Practical cases
S6. Basic programming and computation with R and Python
S7. Applications for real data

Assessment throughout the semester includes: a) Group work weighting 40%, with the possibility of discussion if teachers consider it necessary. The minimum grade for the work is 8.5 points. b)Individual test weighting 60% with a minimum grade of 8.5 points. Minimum attendance at 2/3 of classes Assessment by exam: individual exam that includes the entire subject with a minimum grade of 10 (grade rounded to units). The individual test and exam will be carried out without consulting support sheets, books or other materials, and the use of graphing calculators or cell phones is not permitted; They can only consult the form and tables made available in Moodle for this purpose.

Mandatory

Wooldridge, J.M. (2019), Introductory Econometrics: A Modern Approach, 7th Ed., Cengage Learning. %0 Book
Cameron, A.C., Trivedi, P.K. (2008) Microeconometrics: Methods and Applications. Cambridge University Press
Bruce P., Bruce A., and Gedeck P., (2020), Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Edition,
O' Reilly Media, Inc. - Robert I. Kabacoff, (2022), R in Action: Data analysis and graphics with R, Third Edition, Manning Publications Co. -
Tom Alby, (2024), Data Science in Practice, CRC Press.
Robert I. Kabacoff, (2022), R in Action: Data analysis and graphics with R, Third Edition, Manning Publications Co.

Optional

Ficheiros (slides e scripts) da UC a disponibilizar no Moodle. Eric Goh Ming Hui, (2019), Learn R for Applied Statistics, Apress. Daniel J. Denis, (2020), Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science, JohnWiley & Sons, Inc.

CP1. Information Security:
Fundamentals of security – data security;
Vulnerabilities and threats in security;
IRM – Information Risk Management;
Cryptography – symmetric, asymmetric, hash functions, MAC (Message Authentication Code), digital signatures;
Public Key Infrastructures (PKI).

CP2. Privacy and Personal Data Protection:
The GDPR and Law 58/2019;
Anonymization and pseudonymization techniques.

CP3. Ethics:
Normative ethical theories;
Regulatory dilemmas and gaps in ICT;
Case studies in applied ethics;
The regulation of Artificial Intelligence

The course unit offers two assessment methods: continuous assessment and final exam assessment.

Continuous Assessment (CA) includes:

- Individual quizzes: Throughout the semester, short individual quizzes (approximately 15 minutes each) will be conducted in person, focusing on the material covered in previous classes. The number of quizzes may vary depending on the pace of the course, with their total weight corresponding to 49.5% of the final grade.
- A group project carried out in the context of the privacy module (16.5%).
- A group project in the context of the ethics module (34%).

Each group project must receive a minimum grade of 7 out of 20.
Group project evaluation will focus on content, clarity, and consistency of the analysis.

Final Exam Assessment:

The final exam assessment consists of an individual written exam (100%), held during the official examination period, covering the entire content taught in the course. When necessary, a complementary oral exam may be included.
This assessment method is available to students who choose it or to those who did not pass through continuous assessment.

Mandatory

Andress, J. (2014). The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice. Syngress.
Kim, D., Solomon, M. (2016). Fundamentals of Information Systems Security. Jones & Bartlett Learning.
Cannon, J.C. Privacy in Technology: Standards and Practices for Engineers and Security and IT Professionals. Portsmouth: AN IAPP Publication, 2014.
Breaux, Travis. Introduction to IT Privacy: A Handbook for Technologists. Portsmouth: An IAPP Publication, 2014.
Whitman, M., & Mattord, H. (2013). Management of information security. Nelson Education.
Katz, J., & Lindell, Y. (2014). Introduction to modern cryptography. CRC press.
Ethics, Technology, and Engineering: An Introduction (2011). Ibo van de Poel, Lamber Royakkers, Wiley-Blackwell.
European Union Agency for Fundamental Rights, The Handbook on European data protection law, 2018:, 2019, http://fra.europa.eu/sites/default/files/fra_uploads/fra-coe-edps-2018-handbook-data-protection_en.pdf, http://fra.europa.eu/sites/default/files/fra_uploads/fra-coe-edps-2018-handbook-data-protection_en.pdf

Optional

A. Barreto Menezes Cordeiro, Direito da Proteção de Dados à luz do RGPD e da Lei n.º 58/2019, Edições Almedina., 2020, Cordeiro (2020)
Sara Baase, A gift of fire : social, legal, and ethical issues for computing technology, 2013, -
Whitman, M., Mattord, H. (2017). Principles of Information Security. Course Technology.
Bowman, Courtney. The Architecture of Privacy: On Engineering Technologies that Can Deliver Trustworthy Safeguards. O?Reilly Media, 2015.
Anderson, R. J. (2010). Security engineering: a guide to building dependable distributed systems. John Wiley & Sons.
Zúquete, A. (2018). Segurança em redes informáticas. FCA-Editora de Informática.
Regulamentos e orientações da Comissão Europeia relativos à Proteção de Dados, https://ec.europa.eu/info/law/law-topic/data-protection_en
Bynum, Terrell Ward, and Simon Rogerson, (2004), Computer Ethics and Professional Responsibility: Introductory Text and Readings. Oxford: Blackwell, 2004.
Grupo do Artigo 29, Parecer 05/2014 sobre técnicas de anonimização do grupo de trabalho de proteção de dados do artigo 29.º, de 10 de Abril de 2014, 2014, -, https://ec.europa.eu/justice/article-29/documentation/opinion-recommendation/files/2014/wp216_pt.pdf
Enisa, Orientações da Enisa sobre técnicas de pseudonimização e boas práticas, 2019, -, https://www.enisa.europa.eu/publications/pseudonymisation-techniques-and-best-practices
UE, Proposta do regulamento do parlamento europeu e do conselho que estabelece regras harmonizadas em matéria de inteligência artificial (regulamento inteligência artificial) e altera determinados atos legislativos da União, 2023, -, https://eur-lex.europa.eu/legal-content/PT/TXT/?uri=CELEX%3A52021PC0206
Outros textos a indicar e distribuídos pelo docente ao longo do semestre.

PC1: Overview of Supervised Learning
Typologies
Learning data
Objective functions
Models' assessment and selection
Notes on statistical inference
PC2: Regression Methods
K-Nearest Neighbor
Regression Trees (using CART algorithm)
PC3: Classification Methods
Naive Bayes
K-Nearest Neighbor
Logistic Regression
Classification Trees (using CART algorithm)

The Course can be assessed using the Assessment throughout Semester or Assessment by Exam.

ASSESSMENT THROUGHOUT SEMESTER:
- group quiz online (40%) with a minimum grade of 9
- individual test (60%) with a minimum grade of 9
Approval requires a minimum grade of 10.

ASSESSMENT BY EXAM:
1st part - individual test (60%)
2nd part -individual practical data analysis test, online, with the R software used in classes (40%)
Students have to achieve a minimum grade of 9 in each part of the exam and a combined minimum grade of 10.

Scale 0-20

Mandatory

Gareth, J., Daniela, W., Trevor, H., & Robert, T. (2013). An introduction to statistical learning: with applications in R. Springer.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.
Lantz, B. (2023). Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data. 4th Edition. Packt Publishing.
Larose, D., Larose, C. (2015). Data Mining and Predictive Analytics. John Wiley & Sons.

Optional

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R (2012). Great Britain: Sage Publications, Ltd, 958.

1. Sequences
1.1. Some concepts
1.2. Convergence
1.3. Some limits and useful results
2. Differential calculus in R
2.1. A brief review
2.2. Continuity and limits
2.3. Differentiability and Taylor’s formula; applications
2.4. Numerical methods
2.4.1. Fixed point method
2.4.2. Bisection method
2.4.3. Newton-Raphson method
2.4.4. Numerical differentiation
3. Integral calculus in R
3.1. Anti-derivatives
3.2. Integrals
3.3. Fundamental theorem of calculus
3.4. Numerical integration
3.4.1. Mid-point method
3.4.2. Trapezoidal rule
4. Ordinary differential equations.
4.1. Separable variables
4.2. First order linear equations
4.3. Numerical methods
4.3.1. Euler’s method
4.3.2. Runge-Kutta method (RK4)

A student must obtain an overall grade of at least 10 (out of 20) in one of the following assessment modes:

- Assessment during the semester: a mid-term test (37.5%) + final test (37.5%) + two group works, one about numerical calculus and the other about graphical representation (25%, 12.5% each).

- Exam assessment: in any of the exam seasons (100%).

The exam consists of two parts: analytical (75%) and numerical (25%). Students who have successfully completed the teamwork may skip this numerical component.

The minimum grade is 8. Students with a grade over 16 should be submitted to an oral examination.

Mandatory

[1] Ferreira, J.C. (2011). “Introdução à Análise Matemática”, Fundação Calouste Gulbenkian.
[2] Strang, G. (1991). “Calculus”, Wellesley-Cambridge.
[3] Caputo, H.P. (1973). “Iniciação ao Estudo das Equações Diferenciais”, Livros Técnicos e Científicos Editora, S.A.

Optional

[4] Suleman, A., Rocha, J., Alho, A., Apontamentos de aula. (disponível no Moodle)
[5] Suleman, A., Notas sobre cálculo numérico (disponível no Moodle).
[6] Santos, M.I.R., Matemática computacional (IST).

CP1: What data are and how to think with data.
CP2: Types of problems addressed in Data Science and specificities in the domains of Science, Management, and Society.
CP3: Different traditions and research methodologies and definitions of knowledge acquisition.
CP4: Translating real challenges into technical concepts and using scientifically oriented language.
CP5: The ethical dimension of data use strategies.
CP6: Presentation of practical cases.

This course uses only assessment throughout the semester and does not include exams.
Assessment components:
a) Mini-tests (30%): 6 mini-tests (5% each, the vast majority to be taken at home)
b) Project (30%): group assignment
c) Final test (40%): Written test to be taken during the 1st season, 2nd season or special season (Art. 14, RGACC)

Passing requirement: Final test >= 8 points (out of 20 points)

The final grade for the Project will depend on the code, the reports, and the student's performance in presenting their work.

Mandatory

Cathy O'Neil, Rachel Schutt, Doing Data Science: Straight Talk from the Frontline, 2014, ISBN: 9781449358655,
Borgman, C. L., Big data, little data, no data: scholarship in the networked world, 2015, ISBN: 9780262529914,
Rob Kitchin, The data revolution: Big data, open data, data infrastructures and their consequences, 2014, https://doi.org/10.4135/9781473909472,

Optional

Davenport, T., Harris, J., and Morison, R., Analytics at work: smarter decisions, better results. Harvard Business Review Press, USA., 2010, ISBN: 9781422177693,
Turban, E., Sharda, R., Delen, D., Decision Support and Business Intelligence Systems (9th Eds), 2010, ISBN: 978-0136107293,
Davenport, T., Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, 2014, ISBN: 978-1422168165,

Syllabus contents (SC) articulated with the learning objectives.
SC1. Organization, preparation and transformation of data
SC2. Exploratory data analysis
Missing values
Coding and imputation
Exploratory charts
Random variables
Empirical distribution function
Normal Distribution
SC3. Descriptive data analysis
Descriptive measures
Single and bivariate analysis
Association measures
SC4. Visual representation
Introduction to the principles of visual representation
Visual representation structures

Assessment throughout the semester:
- Individual exercise with R (10%)
- Group work, with presentation and discussion (35%); minimum grade 7.5
- Written test (55%); min. grade 7.5
A minimum attendance of 70% of class hours is required for assessment throughout the semester.

Assessment by exam:
- Exam/individual practical work (40%); minimum grade 7.5
- Written exam (60%); minimum grade 7.5

Mandatory

Brown, D.S. (2022). Statistics and Data Visualization Using R. The Art and Practice of Data Analysis. Sage Publication, Inc.
Cairo, A. (2013). The Functional Art: An introduction to information graphics and visualization (Voices That Matter).
New Riders.
Carvalho, A. (2017). Métodos quantitativos com Excel, Lisboa, Lidel edições técnicas.
Chang, W. (2024) R Graphics Cookbook. 2nd ed. O’Reilly. (Disponível em: https://r-graphics.org/)
Reis, E. (1998). Estatística Descritiva, Lisboa, Sílabo,7ª ed.
Rocha, M. & Ferreira, P.G. (2017) Análise e Exploração de Dados com R. Lisboa, FCA

Optional

Alexandrino da Silva, A. (2006). Gráficos e mapas-representação de informação estatística. Lisboa, Lidel edições técnicas.
Barroso, M., Sampaio, E. & Ramos, M. (2003). Exercícios de Estatística Descritiva para as Ciências Sociais, Lisboa, Sílabo.
Carvalho, A. (2017). Gráficos com Excel - 95 Exercícios, Lisboa, FCA.
Dias Curto, J.J., & Gameiro, F. (2016). Excel para Economia e Gestão. Lisboa, Ed. Sílabo.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London, SAGE Publications Ltd.
Hoaglin, D.C., Mosteller, F & Tukey, J. W. (1992). Análise Exploratória de Dados. Técnicas Robustas, Ed. Salamandra, Lisboa.
Murteira, B. J. F. (1990). Análise Exploratória de Dados. Estatística Descritiva, McGraw Hill.
Wickham, H. (2015). ggplot2: Elegant Graphics for Data Analysis, Springer. (Disponível em: https://ggplot2-book.org/)

CP1. Vectors
The vector space R^n. Inner product and norm. Linear combinations and linear independence. Bases and dimension. Coordinates.

CP2. Systems of Linear Equations
Gaussian elimination method. Classification of linear systems.

CP3. Matrices
Addition and scalar multiplication. Matrix multiplication. Transposition. Inverse matrix and properties.

CP4. Linear Functions
Linear function. Matrix of a linear function. Kernel and image subspaces and the dimension theorem. Basis change.

CP5. Determinants
Definition and properties of the determinant. Determinants and elementary operations.

CP6. Eigenvalues and Eigenvectors
Eigenvalues and eigenvectors. Eigen subspaces. Diagonalization.

Students may choose one of the following assessment methods:
• Assessment throughout the semester:
o 4 in-class mini-tests (20%): written tests performed in class, each lasting 15 minutes;
o Written exam (80%): written exam taken during the 1st exam period, with a minimum grade of 8.5 out of 20.
Students under assessment throughout the semester will receive a grade of zero for any mini-tests not taken.
Mini-tests must be taken in the class group in which the student is enrolled.
mini-tests will be administered either at the beginning or end of the scheduled class, and the rest of the class will proceed as usual.
For the purpose of calculating the final grade for this component, the best 3 out of the 4 mini-tests scores will be considered.
For students choosing this method, the final grade will be the higher of the two: the assessment throughout the semester or the final exam assessment.
• Final exam assessment:
Students will take a written exam worth 100% of the final grade, during either the 1st or 2nd exam period.
The minimum passing grade for this course unit is 10 out of 20.
The course instructors reserve the right to conduct oral exams when necessary.

Mandatory

Strang, G. (2023). Introduction to Linear Algebra (sixth edition) Wellesley-Cambridge Press.
Apoio teórico fornecido pelos docentes. Caderno de exercícios fornecido pelos docentes.

Optional

Lay, D., Lay, S., & McDonald, J. (2016) Linear Algebra and Its Applications (fifth edition) Pearson.

CP1. Functions and parameters
CP2. Variables and control structures
CP3. Invocation and recursion
CP4. Procedures and input/output
CP5. Objects and references
CP6. Object classes
CP7. Composite objects
CP8. Composite object classes
CP9. Arrays
CP10. Matrices

This course is done only by assessment throughout the semester, not considering the modality of assessment by exam. Evaluation components:
a) TPCs (15%): 6 online mini-tests, to do at home;
b) TEST1 (20%): Intermediate written test;
c) PROJECT (25%): Individual project;
d) TEST2 (40%): Written test to be done in 1st season, 2nd season or special season (Art. 14 RGACC)
Approval requirement: TPCs + PROJECT >= 8 points (in 20 points).
The final grade for the PROJECT is determined for each student by an oral test and will depend on the code, the report, and the student's performance in the oral.
Attendance is not an essential requirement for approval

Other relevant information:
- Questions asked in the written tests may involve aspects related to the project.
- It is not possible to pass only by taking the final exam.
- in case of failure in the 1st season, the student can take TEST2 in the 2nd season, keeping the grade of the other components
- When the grade improvement occurs in a school year different from the one in which the work was done, the grade of the components PROJECT, TPCs and TEST1 is replaced by a practical exam, to be performed on a computer before or after the written exam. Students under these conditions who wish to improve their grades should contact the UC coordinator in advance, at least 2 days before the 1st season.

Mandatory

João P. Martins, Programação em Python: Introdução à programação com múltiplos paradigmas, 2013, IST Press, https://istpress.tecnico.ulisboa.pt/produto/programacao-em-python-introducao-a-programacao-utilizando-multiplos-paradigmas/

Optional

PC1. Differential calculus
1.1. Limits and continuity
1.2. Partial derivatives.
1.3. Tangent plane and differentiability.
1.4. The chain rule
1.5. Computation and classification of critical points.
1.6. Gradient descent.
1.7. Linear regression.
PC2. Integral calculus.
2.1. Double integral.
2.2. Double integrals in polar coordinates.
2.3. Application of integral calculus to the evaluation of volume, mass and probability.

Students must obtain an overall grade of at least 10 (out of 20) in one of the assessment modes:
- Assessment throughout the semester: 2 Written Tests (40% each) + participation in class (20%).
- A final Exam (100%) in either the 1st or 2nd examination period.

Mandatory

Stewart, J. "Cálculo - Volume 2", Tradução da 8ª edição norte-americana (4ª edição brasileira), Cenage Learning, 2017.

Optional

Course Content
CC1: Definition and importance of Critical Thinking (CT)
CC2: Basic structure of an argument: premises and conclusion
Examples of simple and complex arguments
CC3: Methods for argument analysis
CC4: Logical fallacies and common reasoning errors
CC5: Criteria for evaluating the quality of arguments
CC6: Argument construction
CC7: Practical applications of CT
CC8: Lateral thinking and the foundations of the Six Thinking Hats model
CC9: Practical applications of each hat: data (white), emotions (red), risks (black), benefits (yellow), creativity (green), thought management (blue)
CC10: Parallel thinking dynamics in academic, professional, and ethical contexts; integration of argumentative methodologies and the Six Hats in simulations, debates, and written exercises

Assessment throughout the semester includes presentations, exercises, debates, readings, and case discussions (in small groups).
Active participation in practical sessions is expected and evaluated according to the following criteria:
Attendance and participation – In-class exercises and group debates (minimum 80% attendance): 20%
Homework assignments – Two tasks: one worth 5%, the other 10%: 15%
Individual essay applying the Six Thinking Hats to a real dilemma or situation: 30%
Final critical reflection, integrating course dimensions and articulating argumentative and parallel thinking: 35%
To successfully complete the assessment throughout the semester, students cannot score less than 7 points in any of the evaluation components listed.
Exam Periods
Written Work - 100%
Although not recommended, it is possible to choose assessment by exam; this assessment may also involve, at the teacher's discretion, an oral discussion (this oral component carries a weight of 40% in the final evaluation).

Mandatory

De Bono, E. (2016). Os Seis Chapéus do Pensamento. Lua de Papel.

Facione, P. A. (2011). Critical Thinking: What It Is and Why It Counts. Insight Assessment.

Fisher, A. (2011). Critical Thinking: An Introduction. Cambridge University Press.

Haber, J., (2020). Critical Thinking, MIT Press

Paul, R., & Elder, L. (2014). The Miniature Guide to Critical Thinking: Concepts and Tools. Foundation for Critical Thinking.

Optional

Brookfield, S. (1987). Developing critical thinkers: challenging adults to explore alternative ways of thinking and acting. San Francisco: Jossey-Bass.

Bowell, T., & Kemp, G. (2002). Critical thinking: a concise guide. London: Routledge.

Cottrell, S. (2005). Critical Thinking Skills: Developing effective analysis and argument. New York: Palgrave McMillan.

Morgado, P. (2003). Cem argumentos: A lógica, a retórica e o direito ao serviço da argumentação. Porto: Vida Económica.

Thayer-Bacon, B.J. (2000). Transforming critical thinking: thinking constructively. New York: Teachers College Press.

Weston, A. (2005). A arte de argumentar. Lisboa: Gradiva

CP1: Data Structures and Algorithms: what are these and why are they important? Abstract Data Types
CP2: Linear data structures: stacks, queues, double-ended queues, lists, and linked lists.
CP3: Introduction to algorithm complexity analysis.
CP4: Search algorithms: linear, and binary search.
CP5: Recursion, iteration, and dynamic programming.
CP6: Iterative sorting algorithms: Bubblesort, Selectionsort, and Insertionsort.
CP7. Recursive sorting algorithms: Mergesort, and Quicksort.
CP8: Nonlinear data structures: tree, binary search trees, AVL trees, and graphs.
CP9: Simple algorithms for nonlinear data structures.

Approval in this course (UC) is only possible through the mode of evaluation during the semester or (for the students with a status awarded by Serviços de Gestão do Ensino that enables them to access the special sitting period) through the special sitting period. There is not, for this course, the evaluation modality of exam.
Evaluation elements and their respective ponderation:
- test 1, written individual -> 30%, minimum mark of 8 values, forecast to happen in the intercalar evaluation period;
- test 2, written individual -> 30%, minimum mark of 8 values, forecast to happen in the first period of exam sitting;
- task 1, individual, with oral examination -> 15%;
- task 2, individual, with oral examination (eventally in groups of 2 students) -> 25%, minimum mark of 8 values.
Thus Final_mark = 30% x Test1_mark + 30% x Test2_mark + 15% x Task1_mark + 25% x Task2_mark.
In the special sitting period (Época Especial) the evaluation elements and their respective ponderation are:
- test, written individual -> 60%, minimum mark of 8 values, and
- two tasks, individual, with oral examination, minimum mark of 8 values each -> 15% + 25%.
Thus Final_mark_special_sitting = 60% x Test_mark + 15% x Task1_mark + 25% x Task2_mark.
To obtain approval in the course (UC) it is required that the Final_mark or the Final_mark_special_sitting is of 10 values out of 20 values.

Mandatory

- J. Wengrow, A Common-Sense Guide to Data Structures and Algorithms, Second Edition. The Pragmatic Bookshelf, 2020.

- M. Goodrich, R. Tamassia, and M. Goldwasser, Data Structures & Algorithms in Python. Wiley, 2013.

Optional

- B. Miller and D. Ranum, Problem Solving with Algorithms and Data Structures using Python, Second Edition, Release 3.0. 2013.

- T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, Fourth Edition. MIT Press, 2022.

- Referências adicionais a indicar durante as aulas.

Programmatic Contents (PC):
PC1: Linear Programming
1.1 General form of a Linear Programming model
1.2 Formulating problems in Linear Programming
1.3 Graphical resolution
1.4 Resolution using general software (Excel Solver)
1.5 Interpreting results and sensitivity analysis
PC2: Integer Linear Programming
2.1 Formulating problems in Integer Linear Programming
2.2 Formulating problems with binary variables
2.3 Resolution using general software (Excel Solver)
2.4 Interpreting results
2.5 Branch-and-Bound algorithm
PC3: Non-Linear Programming
3.1 Formulating problems in Nonlinear Programming
3.2 Resolution using general software (Excel Solver)
3.3 Interpreting results

Assessment throughout the semester or assessment by exam:
1. Assessment throughout the semester:
a) Written test: i) weight of 60%; ii) minimum classification of 8.5;
b) Group project: i) weight of 40%; ii) Groups of 4 students; iii) with oral discussion;
c) Attendance of at least 2/3 of the classes taught;
d) Approval: minimum weighted average of 9.5.
2. Evaluation by Exam (1st and 2nd Season):
a) Written exam: i) weight of 100%; ii) approval: minimum classification of 9.5;
In both assessment methods, an oral discussion may be required.
Scale: 0-20 points.

Mandatory

* Ragsdale, C.T. (2017). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. 8th Ed. Cengage Learning.

Optional

* Evans, J. (2021). Business Analytics. 3rd Ed. Global Edition. Pearson.
* Hillier, F.S and Lieberman, G.J. (2015). Introduction to Operations Research, 10th Ed., McGraw-Hill.
* Ragsdale, C.T. (2001). Spreadsheet Modeling & Decision Analysis: A Practical Introduction to management science. 3rd Ed., South-Western College Publishing.
* Wolsey, L.A. (1998). Integer Programming. Wiley.

CP1. Probability theory: definitions, axioms, conditional probability, total probability and Bayes' formula.
CP2. Univariate random variables: probability and density function, distribution function, parameters.
CP3. Survey methodology: data collection by questionnaire; sampling plan.
CP4. Random vs. Non-random sampling: types of non-random sampling.
CP5. Random sampling: simple, systematic, with unequal probabilities, stratified.
CP6. Combining surveys with big data.

Assessment regime: throughout the semester or by exam.

Throughout the semester:
Intermediate theoretical-interpretive test (35%) (no minimum grade) + final theoretical-interpretative test (35%) (minimum grade 8 val), + practical work in R (30%) (minimum grade 8 val).

By exam:
Theoretical-interpretative test (70%) (minimum grade 8 val) + practical test in R (30%) (minimum grade 8 val).

Mandatory

Groves, R., Fowler, F., Couper, M., Lepowski, J., Singer, E. & Tourangeau, R. (2009) Survey Methodology, 2nd edition, John Wiley and Sons.
Levy, P., Lemeshow, S. (1991). Sampling of Populations: methods and applications. Wiley Interscience.
Reis, E., Andrade, M., Calapez, T. & Melo, P., Estatística Aplicada, volume 1. 7ª edição. Lisboa. Edições Sílabo, 2021, ISBN 978-989-561-186-7.
Reis, E., Andrade, M., Calapez, T. & Melo, P., Estatística Aplicada volume 2, 6ª edição, Lisboa. Edições Sílabo, 2019, ISBN 978-972-618-986-2.
Speegle, D., & Clair, B. (2021). Probability, Statistics, and Data: A Fresh Approach Using R. Chapman and Hall/CRC. Free access at https://mathstat.slu.edu/~speegled/_book/

Optional

Salganik, M. (2018). Bit by Bit- Social Research in the Digital Age. New Jersey: Princeton University Press Verzani, J. (2014). Using R for Introductory Statistics, 2nd Edition, Chapman & Hall/CRC, eBook ISBN 9781315373089. https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf

CP1: Introduction to scientific research: concepts and processes. Research questions. Processes: stages (Identifying the problem; Reviewing the literature; Defining objectives and hypotheses; Selecting the methodology; Collecting data; Analysing data; Conclusions and recommendations).
CP2: Techniques for summarising and analysing scientific articles. Identifying relevant sources, evaluating the literature and synthesising information. Ethics, informed consent, confidentiality and integrity in research. Data collection methods.
CP3: Structure and organisation of research papers: pre-textual elements (cover, title page, abstract, keywords, table of contents), textual elements (introduction, literature review, methodology, results, discussion) and post-textual elements (conclusion, references, appendices, annexes). Preparation of a structure based on topics provided by the lecturer.
CP4: Application of APA Standards in scientific writing and academic reports.

The assessment of the course aims to gauge the students' acquisition of skills in essential aspects of writing texts in an academic context. Assessment throughout the semester includes activities covering different aspects of the technical and scientific writing process, including group and individual work activities:
Group activities (70%) [students are organized into groups of 4, randomly selected].
1- Group discussions with case studies (20%):
Description: each group is given a case study to analyze, and must identify the type of text; the research problem(s), hypotheses, methodologies used and data sources. The results of their work are presented in class to their colleagues (Time/group: presentation - 3 min; debate - 5 min).
Assessment (oral): based on active participation, the quality of the analysis and the clarity of the presentation.
2 - Research exercises and application of APA standards (20%).
Description: Students carry out practical research exercises in a (thematic) context on bibliographical references, their formatting and citation according to APA Norms. Assessment (written work to be submitted on Moodle): The exercises will be corrected and assessed on the basis of accuracy and compliance with APA Standards.
3 - Project Presentation Simulations (30%):
Description: groups choose a topic and create a fictitious project following the structure of a technical report or scientific text, making a presentation of their project in class (Time/group: presentation 3 min.; debate: 5 min.). The work is then reviewed following the comments.
Assessment: (Oral component and written/digital content to be submitted on Moodle): organization, content, correct use of the structure and procedures of academic work, ability to answer questions posed by colleagues and the teacher.
Individual activities (30%):
1 - Summary of a scientific article (20%).
Description: Each student must read and summarize a scientific article.
Assessment: The summaries made in class will be assessed on their ability to identify and summarize the essential elements of the text.
2 - Participation in activities throughout the semester (10%).
Description: This component aims to assess the specific contributions of each student in the activities carried out throughout the semester. Assessment: Interventions in the classroom; relevance of the student's specific contributions to debates; collaborative relationship with colleagues. In order to be assessed throughout the semester, the student must be present at 80% of the classes and have more than 7 (seven) marks in each of the assessments. If there are doubts about participation in the activities carried out, the teacher may request an oral discussion.
Final assessment: In-person written test (100%).

Mandatory

American Psychological Association (2020). Publication manual of the American Psychological Association, 7 edição APA.
Macagno, F. & Rapanta, C. (2021). Escrita académica: argumentação, lógica da escrita, ideias, estilo, artigos e papers. Pactor.
Ribeiro, A. & Rosa, A. (2024). Descobrindo o potencial do CHATGPT em sala de aula: guia para professores e alunos. Atlantic Books.

Optional

Cottrell, S. (2005). Critical thinking skills: developing effective analysis and argument. Palgrave McMillan.
Creswell, J. W., & Creswell, J. D. (2018). Research design: qualitative, quantitative, and mixed methods approaches. SAGE Publications.
D'Alte, P., & D'Alte, L. (2023). Para uma avaliação do ChatGPT como ferramenta auxiliar de escrita de textos académicos. Revista Bibliomar, 22 (1), 122-138. DOI: 10.18764/2526-6160v22n1.2023.6.
Duarte, N. (2008). The art and science of creating great presentations. O'Reilly Media.Creswell, J. W., & Creswell, J. D. (2018). Research design: qualitative, quantitative, and mixed methods approaches. SAGE Publications.
Hofmann, A. (2016). Scientific writing and communication: papers, proposals, and presentations. Oxford University Press.
Kuhn, Deanna (1991). The skills of argument. Cambridge University Press.
Marcos, I.(2016). Citar e referenciar: o uso ético da informação. http://hdl.handle.net/10400.2/3929
Martínez, J. (2016). Cómo buscar y usar información científica: Guía para estudiantes universitários. Santander. http://hdl.handle.net/10760/29934
OIT. (2021). Ajustar as competências e a aprendizagem ao longo da vida para o futuro do trabalho. OIT Genebra.
OIT. (2020). Guia sobre como e porquê recolher e utilizar dados sobre as relações laborais. OIT Genebra.
Rapanta, C., Garcia-Mila, M., & Gilabert, S. (2013). What is meant by argumentative competence? An integrative review of methods of analysis and assessment in education. Review of Educational Research, 83(4), 483-520.
Rodrigues, A. (2022). A Natureza da Atividade Comunicativa. LisbonPress.
Rodrigues, A. D. (2005). A Partitura invisível. Para uma abordagem interacional da linguagem. Colibri.
Swales, J. M., & Feak, C. B. (2012). Academic writing for graduate students: essential tasks and skills. University of Michigan Press.
Umberto, E. (2016). Como se faz uma Tese em Ciências Humanas. Editorial Presença.

Manuais: http://www.apastyle.org/ http://www.apastyle.org/learn/tutorials/index.aspx