Data Science

Fernando Batista

Ana Maria de Almeida

Vítor Basto Fernandes

After the course the student should be able to achieve the Learning Outcomes (LO):
OA1: Account for different definitions of data, different data types and different research approaches that generate it.
OA2: Identify the knowledge claims underlying different interpretations of data.
OA3: Explain the difference between quantitative and qualitative approaches to data generation.
OA4: Examine the implications of data collection for research, business and society.
OA5: Discuss different debates about the implications of data for people in organizations and society.

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.

Helena Soares

Filipe Alexandre Pedra Aguiar de Moura

By the end of the course, each student should be able to:

OA1. Define vectors and explain their properties. Perform operations with vectors in Euclidean space R^n. Define and determine subspaces of R^n, their bases, and dimension.
OA2. Apply the methods of Gauss and Gauss-Jordan elimination to solve and classify linear systems. Interpret their solutions geometrically.
OA3. Give examples of different types of matrices and perform operations with matrices. Formulate relationships between matrices, vectors, and linear systems.
OA4. Recognize a linear transformation. Determine the associated matrices, kernel, and image subspaces. Perform basis changes.
OA5. Calculate determinants. Explain their properties and applications.
OA6. Define and determine eigenvalues and eigenvectors. Diagonalize matrices. Calculate integer powers of diagonalizable matrices.

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 can choose one of the following evaluation methods:

(AS) Assessment throughout the semester:
- 3 in-class mini-tests (20%), with the following rules:
* performed in class throughout the classes period;
* the final grade for this component is the arithmetic mean of the two best mini-tests out of the three mini-tests (each contributing 10%);
* Minimum grade of 10.0 in the arithmetic mean of the two best mini-tests.
- 2 online quizzes (10%), with the following rules:
* performed in Moodle throughout the classes period;
* the final grade for this component will be the average of the obtained grades (each contributing 5%);
* minimum grade of 10.0 in the arithmetic mean of the two quizzes.
- Written exam (70%), in the 1st exam period, with a minimum grade of 8.5.

Final Exam Assessment (EA):
A written exam (100%) during the 1st or 2nd exam period, covering all course material.

Other evaluation rules:
- The CA written exam covers all the material taught and takes place simultaneously with the EA exam in the 1st period.
- Students pass the course if the final grade is 10 or higher.
- The final grade corresponds to the highest grade obtained between CA and EA.
- The course instructors reserve the right to conduct oral exams when necessary.

Diogo Alonso de Freitas

Eugénio Ribeiro

Rodrigo José Bravo Simões

After obtaining approval in the course, students should be able to:
OA1. Develop functions/procedures that implement simple algorithms.
OA2. Develop code that manipulates arrays and objects.
OA3. Develop simple object classes, considering the notion of encapsulation.
OA4. Write and understand Python code.

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.

Abdul Suleman

At the end of this course the student should be able to:
LO1: Calculate limits of sequences
LO2. Compute derivatives and interpret the corresponding result.
LO3. Determine linear and higher order approximations.
LO4. Explicitly compute the antiderivative of some elementary functions.
LO5. Use the fundamental theorem of calculus to differentiate functions defined by integrals.
LO6. Use integrals to compute areas, lengths, probabilities, etc.
LO7. Integrate some notable ODEs.
LO8. Apply some simple numerical methods to compute approximate values and use graphical computational tools.

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 assessment modes:
- Assessment during the semester: Exam (75%) + teamwork on numerical computation (25%).
- 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.

Paula Vicente

At the end of this UC, students should be able to identify types and sources of information appropriate to the research objectives (OA1), critically evaluate the quality of the information obtained (OA2), apply the main sampling methods (OA3) and distinguish between designed data and big data (OA4).

CP1. Types and Sources of Information; Quality of information sources
CP2. Sampling principles and applications
CP3. Designed Data vs Big Data

Assessment regimes: throughout the semester or by exam
Throughout the semester:
- Worksheets (5%)
- Group work: (35%);
- Individual test (60%); minimum grade 7.5
By exam: Individual test (100%)
The teaching team may invite any student to an oral test following the completion of any of the assessment elements.
Students covered by RIIEE must contact the UC coordinator with a view to organizing the learning and assessment processes at the UC.

Maria do Carmo Severino Duarte Grilo Botelho

Luís Orlando Lopes Junqueira

Learning goals (LG) to be developed in articulation with the general objectives:
LG1. Prepare data for analysis.
LG2. Use and interpret a set of statistical tools in the field of descriptive.
LG3. Use Excel, R and Jamovi in data preparation, analysis and representation applications.
LG4. Adapt the visual representation models to different objectives, according to good visualization practices.
LG5. Interpreting and writing the results of a descriptive data analysis.

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 (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

Maria Pinto Albuquerque

At the end of this course, students should be able:
LO1: Identify, rewrite, and examine common forms of data organization and its associated algorithms (with and without dynamic memory management, with iterative or recursive algorithms);
LO2: Know how to evaluate and compare the order of performance and efficiency of a given algorithm and/or data structure for the common operations of inserting, removing, and accessing;
LO3: Identify the most appropriate and efficient data structure for a problem;
LO4: Understand the pros and cons of recursive, and iterative algorithms, as well as dynamic programming.
LO5: Understand different search and sorting algorithms appropriate for computational solutions.

CP1: Data Structures and Algorithms: what are these and why are they important? Abstract Data Types
CP2: Linear data structures: stacks, 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: Basic sorting algorithms: Selectionsort, Insertionsort.
CP7. Advanced sorting algorithms: Mergesort, 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.

Ana Catarina Nunes

Maria João Lopes

At the end of this Curricular Unit, the student is expected to be able to:
LO1. Develop formulations in linear programming, integer linear programming, and non-linear programming for efficiently solve complex problems in real contexts.
LO2. Use general software to determine solutions for problems formulated in linear programming, integer linear programming, and non-linear programming.
LO3. Do the economic interpretation and produce recommendations based on the solutions obtained for problems formulated in linear programming, integer linear programming, and non-linear programming.

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

1. Assessment throughout semester:
a) Written test (60%);
b) Group project with discussion (40%);
c) Attendance of at least 2/3 of the classes.
2. Evaluation by Exam (1st and 2nd Season):
a) Written test (60%);
b) project with discussion (40%);
Approval (in both assessment methods):
i) Requires a minimum mark of 8.5 in the written test;
ii) An oral discussion may be required.
Scale: 0-20 points.

Helena Soares

At the end of the course, each student should be able to:

LG1. Compute partial derivatives and gradients.
LG2. Determine linear approximations of functions of several variables.
LG3. Determine and classify critical points of functions of several variables.
LG4. Apply the previous concepts in the context of regression problems.
LG5. Compute double integrals.
LG6. Apply integral calculus to the evaluation of volume, mass and probability.
LG7. Interpret geometrically all the previous concepts.
LG8. Implement in MATLAB some of the computacional methods studied in class.

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: Written Test (80%) + MATLAB mini-projects (20%).
- A final Exam (100%) in either the 1st or 2nd examination period.

LO1. Develop skills in identifying and understanding the basic processes of scientific research.
LO2. Know, identify and summarise the essential elements of a scientific article.
LO3. Identify the structure of writing in research papers and technical reports. LO4. Know how to use APA Standards in scientific writing and academic reports (standards for dissertations and theses at Iscte-IUL).
The learning objectives will be achieved through practical and reflective activities, supported by the active and participatory teaching method which favours experiential learning. Classes will consist of activities such as:
- Group discussions;
- Oral presentation and defence;
- Analysing texts;
- Project presentations;
- Individual reflection.

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%).

Learning Outcomes
LO1: Explore how critical thinking is essential for informed decisions, problem-solving, and developing strong arguments in academic and professional contexts. Identify situations where it is applied.
LO2: Study the basic components of an argument and how they combine into complex arguments. Distinguish between different types of arguments.
LO3: Develop skills to break down arguments by critically analyzing texts and speeches, identifying strengths and weaknesses.
LO4: Learn to identify logical fallacies and common everyday mistakes. Recognize and correct errors in arguments to improve the accuracy of discourse.
LO5: Develop criteria for evaluating the quality of arguments and argumentative texts, considering coherence, relevance, evidence, and effectiveness in various contexts.
LO6: Practice the construction of solid arguments in oral debates and written texts. Formulate and present clear and structured arguments, adapting them to the audience and context.

CP1. Definition and importance of critical thinking (CT)
• Applications of CT in academic and professional life
• Benefits of CT for decision-making and problem-solving
CP2. Basic structure of an argument: premises and conclusion
• Difference between simple and complex arguments
• Examples of simple and complex arguments
CP3. Methods for analyzing arguments
• Evaluating the validity of the premises
• Identifying assumptions and implications
CP4. Logical fallacies and common errors
• Definition of logical fallacies
• Common types of fallacies (e.g., appeal to authority, false dichotomy)
• Recognizing fallacies in everyday speech
• Strategies to avoid fallacies
CP5. Criteria for evaluating the quality of arguments
• Coherence, relevance, and evidence
• Critical analysis of texts
CP6. Building Arguments
• Creating strong and persuasive arguments
• Practicing argument construction in different contexts
CP7. Practical applications of CT
• Debate exercise

The assessment throughout the semester is done through presentations, exercises, debates, readings, and case discussions (in small groups).
Active participation in the practical classes is expected and follows these criteria:
Attendance/participation - In-class exercises + group debate (with a minimum of 80% attendance) attendance 5%, participation 5%, debate 15% (total 25%)
Homework - 15% (1 homework assignment - 5% + 1 homework assignment - 10%) = 15%
Final Work (60%)

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).

Pedro Ramos

Sérgio Moro

1. Implement distributed and fault-tolerant data storage solutions;
2. Manipulation and extraction of large amounts of information from unstructured databases;
3. To develop soft skills, namely
and Collaboration and Team Work and Critical Observation.

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).

Teresa Calapez

Afonso Fernandes Ribeiro Moniz Moreira

Learning goals (LG) to be developed :
LG1: Consolidate the use of R software in the RStudio environment
LG2: Know how to calculate probabilities in various contexts, including through simulation
LG3: Be familiar with the most common probabilistic behavior models
LG4: Know how to fit probabilistic models
LG5: Understand the principles of statistical inference
LG6: Know how to choose the most appropriate inferential method for each situation

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 Periodical Evaluation or Final Exam.
Evaluation throughout the semester
1. Homework assignments: 10 small exercises (one per week, approximately). The 8 best grades will be considered toward the final grade and will account for 15% of such final grade.
Any assignment not delivered is graded with 0. Homework assignments will be graded on a 0-100 scale. The final grade for this evaluation instrument is obtained multiplying the simple average of the best 8, by 20.
2. One mid-term individual written test, 30% of the final grade, no minimum grade
3. One final individual written test, 30% of the final grade, minimum grade 9 out of 20
4. One final individual computer test, in R, 25% of the final grade, minimum 7 out of 20
OR
Final Exam: computer-lab test (40%): written test (60%). Minimum grades: i) written test, 9 out of 20; ii) computer-lab test, 7 out of 20. Minimum weighted grade of 10 out of 20.
In any case, the final weighted grade, rounded to the units, must be at least 10 ou of 20 in order to succeed

Pedro Ramos

Sérgio Moro

O1: Develop abstraction mechanisms;
O2: Develop Information Modeling abilities;
O3: Develop the ability to extract data from a database in an efficient way.

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.

Mafalda Coutinho de Ponte

Ricardo Noutel de Matos Correia

LG1: Characterize the main unsupervised data methods
LG2: Use R for unsupervised data analytics
LG3: Evaluate, validate and interpret the results

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.

Nuno David

Saul Leite

Secundino Domingos Marques Lopes

LG1. Recognize the main security issues in software-based systems, their causes, and consequences.
LG2. Identify and describe the security services necessary to implement a specific information protection policy based on risk analysis.
LG3. Learn the principles and regulatory frameworks in the domains of personal data protection and privacy, with special focus on the General Data Protection Regulation of 2016.
LG4. Ethically and critically reflect on the implications of technologies and data processing on individuals and society, addressing the resulting challenges in the fields of information security, data protection, and privacy.

CP1. Information Security: Fundamentals of security - data security; Vulnerabilities and threats in security; IRM - Information Risk Management; Cryptography and PKI.
CP2. Privacy and data protection: the General Data Protection Regulation (GDPR) and the Law 58/2019; Anonymisation and pseudonymization techniques.
CP3. Ethics: Ethics and technological development; Computer ethics; Challenges in data science; Responsibility in engineering; Normative ethics and case study.

Assessment is conducted either throughout the semester or by examination.

The semester-long assessment includes:
-1st individual test (33.5%) [CP1]
- 2nd individual test (33.5%) [CP2]
- Group project (31.0%) [CP3]
- Student attendance in classes (2%) [CP3]. To achieve 100% in the attendance component, the student must attend at least 70% of the ethics module classes [CP3].

Each test and the group project have a minimum passing grade of 7 out of 20.

Final exam in the 1st and 2nd exam periods for those not approved through semester-long assessment [CP1, CP2, CP3].

Maria da Conceição Figueiredo

Pedro Nogueira Serrasqueiro

Rodrigo Alvarez

LO1. Understand correlation between variables, simple and multiple linear regression models
LO2. Estimation methods (OLS and ML)
LO3. Residuals assumptions analysis, diagnostic and hypothesis tests
LO4. Lag operator, stationarity, unit root tests, outliers and dummy variables, ARIMA models.
LO5. Extensions of the classical linear regression: non-linear and dynamic models.
LO6. Basic programming and computation with R and Python
LO7. Application of the studied concepts: train/test sets and prediction, information and value extraction from real-world data.

PC1. Regression models
1.1. Correlation
1.2. Simple linear regression
1.3. Multiple linear regression
PC2. Estimation and inference, OLS and ML
PC3. Residual assumptions
3.1. Diagnostic and Hypothesis tests
3.2. Practical cases
PC4. ARMA/ARIMA/SARIMAX models
4. Lag operator, stationarity, unit root test, outliers, dummy variables
4.2. White noise, ARMA, ARIMA and SARIMAX models
4.3. Box-Jenkins methodology, forecasting
PC5. Extensions of the classical regression model
5.1. Non-linear regression
5.2. Practical cases
PC6. Basic programming and computation with R and Python
PC7. Applications for real data
7.1. Train/test split/set, prediction and forecasting
7.2. Practical cases

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 10 points.
b)Individual test weighting 60% with a minimum grade of 8.5 points
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.

Anabela Costa

Ana Catarina Nunes

LG1: Understanding supervised learning methods: scopes of application and procedures
LG2: Use of R software to perform data analysis
LG2: Evaluate and interpret the data analysis results

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

Anabela Costa

Mafalda Coutinho de Ponte

At the end of the Curricular Unit, the student is expected to be able to:

LO1- Discuss challenges faced in real, large scale optimization problems
LO2 - Explain and discuss the available methodologies for addressing hard optimization problems
LO3 - Formulate and design effective solution methods for addressing optimization problems
LO4- Employ the use of advanced tools to solve optimization problems

Programmatic contents (PC):
PC1. MULTIOBJECTIVE PROGRAMMING
1.1. Basic concepts
1.2. Methodologies
PC2. METAHEURISTICS
2.1. Concepts and terminology
2.2. Single point algorithms
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 (100%)

2nd SEASON ASSESSMENT
In the 2nd Season, the Course is assessed through the completion of an Individual Project (100%).

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

Scale 0-20