At the end of the UC, students should be able to simulate probabilistic scenarios (OA1), calculate conditioned probabilities (directly or via Bayes' theorem), and verify the independence of events (OA2). They must know the
classical statistical sampling methods and their applicability conditions (OA3) and know how to calculate estimates and their precision measures (OA4). They should be able to identify contexts of complementarity between survey sampling and big data (OA5).

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.

ME1: Expositional, to present the theoretical reference frames
ME2: Participative, with the discussion of practical cases using R software
ME3: Self-study, related with the autonomous work to be carried out by the student, as is contemplated in the Class Planning.

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.

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.

Maria do Carmo Severino Duarte Grilo Botelho

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

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.

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.

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.

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.

Maria Pinto Albuquerque

Roberto Carlos Medina

Tiago João Dâmaso Pratas

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.

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).

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%).

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].

On the completion of this course the student will be able to
LO1. Classify the networks using correlation and clustering coefficients, distances, centrality measures and heterogeneity measures. Evaluate the network robustness;
LO2. Obtain the co-occurrence network associated with a network representing relations. Analyze of weighted networks; LO3. Choose and characterize the random network models;
LO4. Detect communities and evaluate the methods applied to detect communities.

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 semester or Assessment by exam.
Assessment throughout semester:
i) Group courseworks:
• Weight of 40% in final grade
• Groups of 4 students
• Oral discussion may be required;
ii) Individual Final Test:
• Weight of 60% in final grade
• Minimum grade required 8.5;
iii) Minimum attendance: 2/3 of classes taught
Assessment by exam: 100%
• project (weight of 40% in final grade);
• written exam (weight of 60% in final grade, minimum grade required of 8.5);
• to approve :minimum average >= 9.5
An Oral discussion may be required (for Assessment throughout semester and Assessment by exam)
Scale: 0-20 points.

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.

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

Adriano Martins Lopes

João Pedro Oliveira

At the end of the course, students should be able to:
OA1: understand and know the main platforms for processing large amounts of information
OA2: understand and know how to apply distributed programming/computing models
OA3: understand the stages associated with a machine learning project for large amounts of information
OA4: know how to apply dimensionality reduction techniques
OA5: apply supervised or unsupervised learning techniques to large-scale problems

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.

Pedro Ramos

Frederico Henriques Antão Mendes Tremoço

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.

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

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).

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

At the end of the course, each student should be able to:
LO1. Define the objectives and formulate the CD tasks that allow the client to extract the desired knowledge.
LO2. Define the data variables and metadata that lead to the required knowledge.
LO3. Plan the different phases of project development.
LO4. Process the data with the most appropriate Data Science tools to achieve the proposed objectives.
LO5. Produce data visualisations and documents suitable for correctly communicating the results obtained.
LO6. Solve problems inherent in using real data from an ethics-by-design perspective.

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.

Jorge Louçã

After finishing this unit a student should be able to:
LG1. Know and understand basic concepts and technologies for Web development.
LG2. Know and understand interface technologies between a Web application and a Database.
LG3. Model and develop a Web application allowing to manage persistant data from human interaction with software on the Web.

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.

On completion of this course, students should:

LG1. Understand the principles and methods of stochastic simulation;
LG2. Be able to develop efficient algorithms for generating pseudorandom numbers;
LG3. Be able to apply the Monte Carlo method;
LG4. Understand and be able to apply different Monte Carlo via Markov Chains methods;
LG5. Be able to implement resampling techniques;
LG6. Be able to simulate a real system through discrete event simulation;
LG7. Be able to analyze and evaluate simulation results;
LG8. Be able to implement efficient stochastic simulation algorithms in R.

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 - Two mini coursework of 2 students (20%);
3 - Final Test (50%).
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%).

The course introduces the major themes of (mostly) Symbolic Artificial Intelligence and Machine Learning, from an essentially applied perspective, bearing in mind the major context provided by the data science degree, the knowledge and skills acquired in the other courses, and the fundamental objectives and requirements of the data science degree.
The three major topics of the program are logic programming, mostly symbolic adaptive techniques for the representation of adaptive world models, and symbolic machine learning algorithms to learn world models.
After the students have completed the course, they must
? Be fully aware of the existence of mainly symbolic paradigms for the representation and autonomously learning of adaptive world models.
? Have mastered the capability to decide whether to use the paradigms learned in the course to application problems / domains whenever suited.

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%).

The aim is to provide students with experience in applying solutions to concrete, data-driven problems in a variety of contexts and domains. Students will learn how to develop a data science project in an academic and scientific environment, identifying needs, manipulating data, and identifying the appropriate methodologies for the problem to be solved. A number of aspects, potentially relevant for a project with large (real) data, will be reinforced, such as: (geospatial) visualisation, automatic classification methods, data balancing and overfitting, time series, also emphasising the correct way to communicate results. Preference is given to receiving problems from Iscte teachers/researchers with real and delimited projects.

S1: Introduction to potentially relevant elements for project development
S2: Introduction to applied Data Science projects
S3: Framework and choice of appropriate methodology
S4: Development of the project with large and preferably real data
S5: Templates for the dissemination of obtained results

Given its nature, this course is evaluated only by throughout semester assessment and does not include assessment by exam. The assessment consists of two components:
a) Quizzes (40%): 2 group quizzes, with consultation and taken in class (5% each) + 1 group quiz with consultation (30%);
b) Group work (60%): weekly monitoring (10%) + report writing and oral presentation (50%).

Students must obtain a minimum mark of 9 in each component.

Scale 0-20

Upon successfully completing this course, the student is expected to be able to:

LO1. Understand the structure and characteristics of longitudinal data and repeated measures.
LO2. Understand the most common models for time series.
LO3. Apply and interpret mixed-effects regression models (linear and non-linear models).
LO4. Understand growth modeling and change in longitudinal data.
LO5. Analyze longitudinal data using computational tools such as R.
LO6. Develop a critical approach to selecting appropriate models and interpreting the results.

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 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. To approve a weighted average of at least 9.5 points in 20 is required.
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.

OA1. Understand the Field of Deep Learning
OA2. Aquire Knowledge of the Different Neural Network Architectures
OA3. Develop Proficiency in Implementing and Training Neural Networks
OA4. Understand to the Transformer Architecture
OA5. Understand Generative Models

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.