LG1. Review the concept of function and its properties. Types of functions and operations with functions.
LG2. Graphics of elementar functions and function transformations.
LG3. Limits, indeterminations and graphic interpretation. Continuity.
LG4. Derivatives and its applications. Graphic interpretation.
LG5. Linear approximations and higher order approximations.
LG6. Derivative of composed functions and inverse functions.
LG7. Calculations with matrices and vectors.
LG8. Calculating detrminants and applicating its proprieties.
LG9. Knowing the concept of linear transformation and representation with matrices.
LG10. Calculating eigenvalues and eigenvectors.

PC1. Function. Elementar functions, Different type of functions. Operations with functions. Logaritmic and trigonometric functions.
PC2. Limits of a function at a point, Continuity at a point. Assimptotic lines.
PC3. Derivative of a function at a point. Derivative rules. Optimization problems.
PC4. Derivative of composed functions – chain rule. Derivative of the inverse function.
PC5. Linear approximation and Taylor approximation.
PC6. Solving linear equation systems. Matrices and operations. Inverting matrices. Determinants and properties. Linear transformations.
PC7. Real vector space. Inner product. Parallelism and perpendicularity.
PC8. Eigenvalues, eigenvectores and matrix diagonalization.

Passing with a grade not lower than 10 points in one of the following modalities:
- Assessment throughout the Semester:
* 7 assignments/mini-tests conducted during classes. The best 5 are counted, each with a weight of 5% (total of 25%).
* autonomous work, with a weight of 5%.
* applied mathematics project, with a weight of 10%.
* Final test to be conducted on the date of the first exam period, with a weight of 60% and a minimum grade of 8 points
or
- Examination Assessment (100%).

There is the possibility of conducting oral examinations.
Grades above 17 points must be defended orally.

The learning objectives (LO) of this Course Unit are:
LO1. Know in a general way the main aspects of human anatomy, physiology and pathophysiology that explain the origin and expression of diseases.
LO2. Identification of symptoms and signs and the importance of semiology as a key element for some diagnoses. Know what diagnostic algorithms are, and the importance of accurate diagnosis.
LO3. Know in detail four models of common diseases to integrate aspects related to the context of present and future medicine, diagnostic technologies, therapy (including digital therapy) and follow-up.

This UC has the following contents (CPs):
CP1. Elementary notions of anatomy, physiology and pathophysiology
CP2. Semiology and diagnostic algorithm.
CP3. Model diseases (Dementia, COPD, Diabetes Mellitus, Breast Cancer)

There are two possible forms of assessment: periodic evaluation and final exams.
Periodic evaluation:
- Elaboration of group work (30%);
- Presentation of group work (20%);
- Participation in class (10%);
- Written test (40%)

The minimum grade for validation of the evaluation in all components is 8 points.
Final exam: individual written test (100%)

The learning objectives (LO) of this Course Unit are:
OA1. Allow the student to understand the relevance and challenges that exist in the field of information systems in the health sector;
OA2. Identify the main technologies in terms of information systems that impact the decision-making process and management in the health area;
OA3. Allow the student to understand the concepts of quality, data collection and management and information structures, within the organizational scope (clinical and administrative), with a structured and analytical view;
OA4. Methodologies and tools for modeling business processes and recording clinical practice;
OA5. Understand why organizations need to know and model their activity processes;
OA6. Recognize the components of a health information ecosystem, in the context of the main management processes and clinical workflow;

This UC holds the following syllabus (CPs):
CP1. Modeling of business processes in information systems (IS);
CP2. Information Architecture, notions of databases (relational and others) and the use of data extraction tools (ETL);
CP3. Business architecture as a linking tool between healthcare processes and related systems and data;
CP4. Most common information systems in the SNS and non-SNS;
CP5. Security and information segregation in the context of Health,
CP6. Information systems teams in health organizations;
CP7. IS support in the administration function and in the clinical function;
CP8. Models of maturity and digital adoption in healthcare (eg HIMSS);
CP9. Critical Success Factors and Project Implementation Matrix, CRUD and RACI matrix.

Resolution of between 2-4 exercises/case analysis/individual challenges (R)
2 group assignments (TG1 + TG2) - Groups between 4-6 students.
1 oral presentation/individual written presentation (A)
Final grade = R*0.3 + TG1-2*0.5 + A*0.2
Students who were not successful in the periodic assessment (minimum 10 points) are submitted to a recourse exam worth 100% of the grade.

At the end of the course, students should be able to:
LO1: Create and Manipulate Data Structures
LO2: Apply the most appropriate sorting and search algorithms for a specific problem
LO3: Analyze the complexity and performance of an algorithm
LO4. Identify, implement, and analyze the most appropriate data structures and algorithms for a certain problem

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

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

By the end of this course unit, the student should be able to:
LO1: Apply fundamental programming concepts.
LO2: Create procedures and functions with parameters.
LO3: Understanding the syntax of the Python programming language.
LO4: Develop programming solutions for problems of intermediate complexity.
LO5: Explain, execute and debug code fragments developed in Python.
LO6: Interpret the results obtained from executing code developed in Python.
LO7: Develop programming projects.

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

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

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

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

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

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

LG1 Dominate the concepts of sequence and numerical series
LG2 Calculate limits of sequences and, relative to a series, find out the existence of sum
LG3 Understand the generalization of the concept of series to functional series and obtain the convergence domain
LG4 Understand the definition of integral as the limit of Riemann sums
LG5 Calculate primitives and apply them to determine the value of integrals
LG6 Apply integrals to calculate areas, lengths and mean values
LG7 Solve 1st order linear ordinary differential equations (ODEs)
LG8 Calculate partial derivatives and directional derivative
LG9 Interpret the gradient vector as the direction of maximal increase of a function
LG10 Decide about the existence of a tangent plane
LG11 Obtain the 1st order Taylor development and, explore numerically in higher order
LG12 Obtain unconstrained extrema(otimization)

PC1 Sequences. Monotony. Bounded sequences. Geometric progression
PC2 Convergence of sequences
PC3 Numerical series, partial sums and sum
PC4 Convergence criteria of series of non-negative terms
PC5 Simple and absolute convergence of alternating series. Leibniz's criterion
PC6 Power series and domain of convergence
PC7 Antiderivatives. Riemann definite integral. Fundamental theorem of calculus
PC8 Integration by parts and change of variables. Decomposition into simple fractions
PC9 Applications of integral (area, length, mean value)
PC10 Improper integral and convergence
PC11 First order linear ODE
PC12 Multivariable real functions. Level curves. Limits and continuity
PC13 Partial derivatives at a point and gradient vector. Linear approximation, tangent plane and differentiability
PC14 Directional derivative. Chain rule. Taylor's polynomials and series
PC15 Quadratic forms and otimization problems

Approval with a grade >= 10 points (out of 0 to 20) in one of the following modalities:
- Assessment throughout the semester:
* 4 mini-tests conducted during classes, each with a weight of 5% (total of 20%).
* Mid-semester test (15%).
* Autonomous work (5%).
* Final test (60%) on the date of the first exam period, with a minimum grade of 8 points.

Or

- Assessment by exam (100%).

There is the possibility of conducting oral exams.
Grades above 17 points must be defended orally.

LO1. Define requirements for a technological project using a user-centred design approach.
LO2. Plan, manage, and execute the project using an agile project management methodology based on sprints.
LO3. Work effectively and efficiently in an interdisciplinary team, being able to resolve conflicts and define the various roles within the group, including leadership roles.
LO4. Develop the technological project according to user requirements, using hardware kits and low-code or no-code software tools.
LO5. Apply standards for the preparation of technical reports.
LO6. Prepare the demonstration of the technological project’s functionalities.

S1. Introduction, course overview and assessment. Demos of previous year’s projects and new project ideas.
S2. Hardware and software kits available in the laboratory.
S3. Condensed Design Sprint methodology: Map, Sketch and Decide in 140 minutes.
S4. AEIOU interviews, empathy maps, personas.
S5. Use scenarios, definition of user requirements and product vision.
S6. Team management, conflict resolution, and leadership.
S7. Project management: Agile vs Waterfall methodologies. Introduction to the PMBook.
S8. Agile SCRUM methodology: 3 roles, 5 ceremonies, 3 artefacts, and 4 bi-weekly sprints.
S9. Product backlog, functionalities, user stories, prioritisation using MoSCoW, cost estimation with story points.
S10. Tools supporting SCRUM (JIRA).
S11. Low-code/no-code tools for prototype development (Power Apps).
S12. Hardware simulator.
S13. Production of a technical report and project demonstration.

The course is assessed continuously throughout the semester, with no final exam. Weighting is as follows:
• Individual assessment: 2 mini-tests – 20%
• Group project assessment – 80%, based on deliverables submitted via Moodle:
 ◦ D1 – User Requirements Report and Product Vision – 20%
 ◦ D2–D5 – Interim automated reports generated by JIRA at the end of Sprints #1 to #4 – 20%
 ◦ D6 – Final Technical Report – 15%
 ◦ D7 – Presentation, prototype demo, and project discussion – 25%
Students pass the course if they achieve a final weighted mark equal to or greater than 9.5 out of 20.

LG1 - Know and use the main concepts of descriptive statistics, choose appropriate measures and graphical representations to describe data
LG2 - Apply basic concepts of probability theory, namely compute conditional probabilities, and check for independence of events
LG3 - Work with discrete and continuous random variables.
LG4 - Work and understand the uniform, Bernoulli, binomial, Poisson, Gaussian distribution, as well as Chi-Square and t distribution
LG5 - Perform point parameter estimation and distinguish parameters from estimators
LG6 - Build and interpret confidence intervals for parameter estimates
LG7 - Understand the fundamentals of hypothesis testing
LG8 - Get familiar with some software (such Python or R)

Syllabus contents (SC):
SC1 - Descriptive statistics: Types of variables. Frequency tables and graphical representations. Central tendency measures. Measures of spread and shape.
SC2 - Concepts of probability theory: definitions, axioms, conditional probability, total probability theorem and Bayes's formula
SC3 - Univariate and bivariate random variables: probability and density functions, distribution function, mean, variance, standard deviation, covariance and correlation.
SC4 - Discrete and Continuous distributions: Uniform discrete and continuous, Bernoulli, binomial, binomial negative, Poisson, Gaussian, Exponential Chi-Square and t distribution.
SC 5 - Sampling: basic concepts. Most used sample distributions
SC6 - Point estimation and confidence intervals
SC7 - Hypothesis testing: types of errors, significance level and p-value

Approval with a mark of not less than 10 in one of the following methods:
- Assessment throughout the semester: 1 mini-test taken during the lessons (15%) + Final written test taken on the date of the 1st period (60%) + autonomous work (5%) + group project (20%),
All assessment components, except for the autonomous work, are mandatory and require a minimum grade of 8 out of 20.
or
- Assessment by Exam (100%).

Notes:
1) A complementary oral assessment may be conducted after any evaluation moment to validate the final grade.
2) For this Course Unit, students must consult the Iscte Academic Code of Conduct, available on the institutional platforms, to ensure compliance with the established ethical and behavioral standards.

LO1: Understand the main theories, concepts, and issues related to Work, Organizations, and Technology;
LO2: Understand the main processes of the digital transition directly related to the world of work and its organizations;
LO3: Analyze the multiple social, economic, and political implications brought by the digital transition;
LO4: Explore cases, strategies, and application methods to understand the real impacts of the digital transition on professions, companies, and organizations.

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

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

Individual work accounting for 35% of the final grade.

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

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

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

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

OA1 - To be trained in the ethical and responsible use of Generative Artificial Intelligence (AI) tools
OA2 - To acquire critical analysis skills on the results produced by Generative AI tools
OA3 - To be able to identify and develop creative solutions in solving ethically and socially complex problems with Generative AI
OA4 - To be able to apply Generative AI tools in the preparation of academic work, in particular in the application of academic writing and in the use of normative citation and referencing procedures.

CP1 - Introduction to AI and Generative AI:
* Theoretical exposition on the historical context, evolution and important concepts about Artificial Intelligence (AI) and Generative AI
CP2 - Prompt Engineering:
* Explanation of good practices for interacting with generative language models
CP3 - Generative AI Tools:
* Exploration of multiple Generative AI tools, based on text, images and videos
CP4 - Formation of argumentative content:
* Development of creative solutions using argumentation practices and Generative AI tools
CP5 - Rules for scientific writing:
* Application of citation and referencing standards (APA standards) in academic writing

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

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

LO1. Acquiring knowledge about the fundamentals and stages of the Design Thinking process
LO2. Develop skills such as critical thinking, collaboration, empathy and creativity.
LO3. To apply Design Thinking in problem solving in several areas, promoting innovation and continuous improvement.

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

Semester-long Assessment Mode:

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

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

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

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

LO1. Develop specific oral communication skills for public presentations.
LO2. Know and identify strategies for effective use of the vocal apparatus.
LO3. Identify and improve body expression. LO4. Learn performance techniques.
The learning objectives will be achieved through practical and reflective activities, supported by an active and participatory teaching method that emphasizes experiential learning. The knowledge acquired involves both theatrical theory and specific oral communication techniques. Students will learn about the fundamentals of vocal expression, character interpretation and improvisation, adapting this knowledge to the context of public performances.

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

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

The learning objectives (LO) of this Course Unit are:
OA1. Allow the student to understand the relevance and challenges that exist in the field of information systems in the health sector;
OA2. Identify the main technologies in terms of information systems that impact the decision-making process and management in the health area;
OA3. Allow the student to understand the concepts of quality, data collection and management and information structures, within the organizational scope (clinical and administrative), with a structured and analytical view;
OA4. Methodologies and tools for modeling business processes and recording clinical practice;
OA5. Understand why organizations need to know and model their activity processes;
OA6. Recognize the components of a health information ecosystem, in the context of the main management processes and clinical workflow;

This UC holds the following syllabus (CPs):
CP1. Modeling of business processes in information systems (IS);
CP2. Information Architecture, notions of databases (relational and others) and the use of data extraction tools (ETL);
CP3. Business architecture as a linking tool between healthcare processes and related systems and data;
CP4. Most common information systems in the SNS and non-SNS;
CP5. Security and information segregation in the context of Health,
CP6. Information systems teams in health organizations;
CP7. IS support in the administration function and in the clinical function;
CP8. Models of maturity and digital adoption in healthcare (eg HIMSS);
CP9. Critical Success Factors and Project Implementation Matrix, CRUD and RACI matrix.

Resolution of between 2-4 exercises/case analysis/individual challenges (R)
2 group assignments (TG1 + TG2) - Groups between 4-6 students.
1 oral presentation/individual written presentation (A)
Final grade = R*0.3 + TG1-2*0.5 + A*0.2
Students who were not successful in the periodic assessment (minimum 10 points) are submitted to a recourse exam worth 100% of the grade.

At the end of the learning unit, the student must be able to: LG.1. Understand entrepreneurship; LG.2. Create new innovative ideas, using ideation techniques and design thinking; LG.3. Create value propositions, business models, and business plans; LG.5. Develop, test and demonstrate technology-based products, processes and services; LG.6. Analyse business scalability; LG.7. Prepare internationalization and commercialization plans; LG.8. Search and analyse funding sources

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

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

"To succeed in this course, students should be able to:
LO1. Understand the fundamental principles of biomedical signals and their generation in the human body.
LO2. Relate the properties of physiological signals to individuals' clinical status.
LO3. Identify and characterize intelligent sensors for physiological signal acquisition.
LO4. Implement methods for processing and analyzing biomedical signals.
LO5. Evaluate and compare different analytical approaches for interpreting collected data."

"CP1. Introduction to biomedical signals and their importance in healthcare. Practical examples and applications.
CP2. Methods for acquiring and digitizing physiological signals using sensors and devices.
CP3. Fundamentals of biomedical signal analysis with a focus on practical interpretation.
CP4. Pre-processing and filtering techniques for signals using intuitive software.
CP5. Basic concepts of statistics applied to physiological signals with visual interpretation.
CP6. Recording and analysis of brain activity based on typical patterns.
CP7. Cardiac monitoring and electrocardiogram reading through case studies.
CP8. Interpretation of muscle activity from electromyographic signals without advanced calculations.
CP9. Analysis of autonomic responses and electrodermal activity applied to healthcare.
CP10. Introduction to IoT sensors for acquiring and processing physiological signals."

"Continuous Assessment:
a) Practical Project (60%) – Group work with parcial delivery, a technical report and presentation.
b) Written Test (40%) – Theoretical assessment on concepts and methods covered in the course. Minimum grade of 8 out of 20.

Exam Assessment:
a) For students who fail continuous assessment, choose exam assessment, or wish to improve their grade – individual written test with a weighting of 100%, and a minimum grade of 10 out of 20.

The grades for the assignments may vary based on individual performance demonstrated in an oral discussion, which will take place (for the group) if the difference between the test and assignment grades (for any member) exceeds 3 points.

A minimum attendance of at least two-thirds of the classes is required."

At the end of this UC, the student should be able to:
LG.1. Present the image of the product/service in a website
OA.2. Present the image of the product/service in social networks
OA.3. Describe functionalities of the product/service
OA.4. Describe phases of the development plan
OA.5. Develop a prototype
OA.6. Test the prototype in laboratory
OA.7. Correct the product/service according to tests
OA.8. Optimize the product/service considering economic, social, and environmental aspects
OA.9. Adjust the business plan after development and tests, including commercialization and image
OA.10. Define product/service management and maintenance plan

I. Development of the product/service image
II. Functionalities of the product/service
III. Development plan
IV. Development of the product/service (web/mobile or other)
V. Revision of the business plan
VI. Management and maintenance of the product/service
VII. Certification plan
VIII. Intellectual property, patents, and support documentation
IX. Main aspects for the creation of a startup - juridical, account, registry, contracts, social capital, obligations, taxes

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

The learning objectives (LO) of this Course Unit are:
LO1. Allow the student to have knowledge about telehealth, its concepts, definitions and applications.
LO2. Know the role of mobile technologies and issues related to portability in the context of Telehealth LO3. Allow the student to realize the relevance and challenges in the field of solutions for assisted health technologies;
LO4. Know the principles and know how to use intelligent sensor networks based on edge and cloud computing for the acquisition of physiological and environmental signals

This UC has the following contents (CPs):
CP1. Telehealth and Assistive Technologies for health: Concepts, definitions and typology;
CP2. Models of interaction in Telehealth (real time, deferred, etc.);
CP3. General architecture of an IoT system for health: biomedical sensors, acquisition and computing platforms, communication protocols for sensors and actuators, and information systems;
CP4. Conception and design of wearable devices in terms of usability and ergonomics;
CP5. Signal acquisition and processing
CP6. Case studies in telehealth and in assistive technologies for health

Resolution of 2 to 4 exercises/case analysis/individual challenges (R)
2 group assignments (TG1 + TG2) - Groups between 4-6 students.
1 oral presentation/individual written presentation (A)
Final grade = R*0.3 + TG1-2*0.5 + A*0.2
Students who were not successful in the periodic assessment (minimum 10 points) are submitted to a recourse exam worth 100% of the grade

To succeed in this course, students should be able to:
LO1. Develop and apply techniques for preparing and analysing exploratory data
LO2. Develop and apply computer vision, knowledge transfer and data augmentation techniques
LO3. Understand the application of image classification techniques such as Neural Networks and Convolutional Neural Networks.
LO4. Understand the application of classification techniques such as Decision Trees, Neural Networks and Bayesian Classification.
LO5. Apply rule induction techniques to knowledge extraction problems
LO6. Develop and apply time series techniques for analysing and forecasting data.
LO7. Determine assessments for good modelling.

PC1. Introduction to data mining, decision problems and applications
PC2. The data cycle process.
PC3. Review of descriptive and exploratory statistics.
PC4. Operations with images
PC5. Extraction of image characteristics
PC6. Classical neural networks
PC7. Convolutional neural networks
PC8. Knowledge transfer
PC9. Decision trees.
PC10. Bayesian inference techniques.
PC11. Time series

Assessment will take place throughout the semester, through two group assignments, each worth 30% of the final grade, and a written test worth 40%.
Each of the assessment components has a minimum mark of 8, and a final mark of at least 10 is required to pass the course.
The marks for the assignments may vary depending on the performance demonstrated individually in an oral discussion, which will take place (for the group) if the mark (of one of the members) between the test and the assignment has a difference of more than 3 points.
Given the practical nature of the course, there is no exam.
A minimum attendance of no less than 2/3 of the classes is required.
Grades can only be improved by repeating the assessment process the following year.

The learning objectives (LO) of this Curricular Unit are to enable students to acquire the following competencies:
LO1: Recognize Hospital Data Types
LO2: Tackle Data Challenges
LO3: Apply AI for Insight Extraction
LO4: Enhance Data Quality:
LO5: Implement AI Techniques
LO6: Navigate Ethical and Legal Aspects
LO7: Communicate Insights Effectively
LO8: Collaborate on AI Projects:
LO9: Integrate AI Tools and Libraries
LO10: Healthcare Applications

This CU has the following programmatic contents (PCs):
PC1: Introduction to Hospital Data
* Definition and key concepts
PC2: Data Challenges in Healthcare
PC3: AI Foundations for Knowledge Extraction
PC4: AI Techniques for Healthcare Data Analysis
PC5: Ethical and Legal Considerations
PC6: Effective Communication of Findings
PC7: Collaborative AI Projects in Healthcare
PC8: Tools and Libraries for Healthcare Data
PC9: Healthcare Applications of AI
PC10: Project Presentation and Reflection

Assessment will preferably take place continuously throughout the semester (1st Period), consisting of two group assignments, each worth 30% of the final grade, and a written test worth 40%. Each component requires a minimum score of 8 out of 20, and a final weighted average of at least 10 is required to pass the course. A minimum attendance of two-thirds of the classes is also required.
Assignment marks may be adjusted based on individual performance demonstrated during an oral discussion. This may take place if there is a discrepancy greater than 3 points between a student's test score and their assignment score.
Exam Period Assessment (2nd Period and Special)
In the Exam Period, students may opt for an alternative assessment consisting of:
An individual project, worth 60% of the final grade.
A written individual test, worth 40%.
Both components are mandatory and require a minimum score of 8 out of 20. A weighted average of at least 10 is required to pass the course.

At the end of the curricular period of this course, the students will be able to:
LO1. Identify various management models and theories, as well as their effects on work and organizational performance.
LO2. Understand the different functions of management.
LO3. Understand the specifics of the Health sector, Health and healthcare service delivery.
LO4. Comprehend the specifics of healthcare organizations' management.
LO5. Grasp the potential impact of digitization on healthcare organizations' management.

CP1. Management theories
CP2. Management Functions
CP3. Economic features of the Health sector, of healthcare provision and of Health Organizations
CP4. Management functions (planning, organizing, leading and controlling) in Health Organizations

There are two forms of assessment for this course: assessment throughout the semester and final exam.

Evaluation throughout the semester:

Completion/presentation of group assignments (40%);
Mini written tests throughout the semester (60%).
The minimum passing grade for validation of the assessment throughout the semester in each and all components and sub-components is 7.5 points.

Final Exam: Individual written exam (100%).

LO 1 Explain what databases and information systems are, characterising them in terms of both technology and their importance to organisations.
LO 2. formally represent information requirements by drawing up conceptual data models.
LO 3 Explain the Relational Model and data normalisation, highlighting their advantages and the situations in which they should be applied.
LO 4 Design relational databases that respond to requirements specified by conceptual data models.
LO 5. build and programme a relational database using the SQL language.
LO 6. manipulate data - i.e. insert, query, alter and delete - using the SQL language.
LO 7. Explain what database administration consists of, why it is necessary and how its most essential tasks are carried out.

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

Assessment throughout the semester:

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

Assessment by exam:
- One exam, weighted 100%

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

LG1 - Know and use the main concepts of descriptive statistics, choose appropriate measures and graphical representations to describe data
LG2 - Apply basic concepts of probability theory, namely compute conditional probabilities, and check for independence of events
LG3 - Work with discrete and continuous random variables.
LG4 - Work and understand the uniform, Bernoulli, binomial, Poisson, Gaussian distribution, as well as Chi-Square and t distribution
LG5 - Perform point parameter estimation and distinguish parameters from estimators
LG6 - Build and interpret confidence intervals for parameter estimates
LG7 - Understand the fundamentals of hypothesis testing
LG8 - Get familiar with some software (such Python or R)

Syllabus contents (SC):
SC1 - Descriptive statistics: Types of variables. Frequency tables and graphical representations. Central tendency measures. Measures of spread and shape.
SC2 - Concepts of probability theory: definitions, axioms, conditional probability, total probability theorem and Bayes's formula
SC3 - Univariate and bivariate random variables: probability and density functions, distribution function, mean, variance, standard deviation, covariance and correlation.
SC4 - Discrete and Continuous distributions: Uniform discrete and continuous, Bernoulli, binomial, binomial negative, Poisson, Gaussian, Exponential Chi-Square and t distribution.
SC 5 - Sampling: basic concepts. Most used sample distributions
SC6 - Point estimation and confidence intervals
SC7 - Hypothesis testing: types of errors, significance level and p-value

Approval with a mark of not less than 10 in one of the following methods:
- Assessment throughout the semester: 1 mini-test taken during the lessons (15%) + Final written test taken on the date of the 1st period (60%) + autonomous work (5%) + group project (20%),
All assessment components, except for the autonomous work, are mandatory and require a minimum grade of 8 out of 20.
or
- Assessment by Exam (100%).

Notes:
1) A complementary oral assessment may be conducted after any evaluation moment to validate the final grade.
2) For this Course Unit, students must consult the Iscte Academic Code of Conduct, available on the institutional platforms, to ensure compliance with the established ethical and behavioral standards.

LO1: Explain the concepts and components of analytical information systems and Business Intelligence.
LO2: Model and transform data using Power BI.
LO3: Create interactive reports and dashboards tailored to different management needs.
LO4: Analyze and interpret data to support organizational decision-making.
LO5: Evaluate the quality of information and the effectiveness of analytical visualizations.

CP1: Introduction to Business Intelligence – concepts, architecture, and tools.
CP2: Data integration and transformation – Power Query, M, connections to multiple sources.
CP3: Data modeling – relational tables, DAX, measures, and KPIs.
CP4: Data visualization – creating reports, dashboards, storytelling with data.
CP5: Assessment and best practices in BI projects – data ethics, governance, information quality.

Assessment throughout the semester includes:

- Practical project for developing an analytical information system carried out in a group (maximum of 3 students), including a report (15%) with a mandatory midterm submission (15%) and an oral defense with an individual grade (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- Weekly in-class and independent assignments (20%).

The use of AI tools (e.g., ChatGPT) is permitted for technical support and must be clearly referenced with the prompts used.

The Final Exam is a written, individual, closed-book exam covering all the material.

Those who do not opt ​​for assessment throughout the semester take the final exam in Period 1.

Those who did not successfully complete the assessment throughout the semester, or the exam in Period 1, with a score greater than or equal to 9.5 (out of 20), take the final exam in Period 2.

"LO1: Define the main concepts, steps and methods involved in the development of Text Mining processes for the Healthcare sector.
LO2: Segment documents, create dictionaries, and perform other pre-processing tasks to prepare text for classification tasks relevant in healthcare.
LO3: Select and justify appropriate techniques for specific text processing tasks.
LO4: Construct vector representations from texts.
LO5: Explain the operation of algorithms for text classification, such as Naïve Bayes or KNN.
LO6: Apply a classifier to real cases.
LO7: Group documents using the k-means algorithm.
LO8: Develop prompt engineering in LLMs."

"SY1: Usefulness of large amounts of text, challenges and current methods in healthcare.
SY2: Unstructured vs. (semi-)structured information: Electronic health records, clinical notes and medical examination reports.
SY3: Information retrieval and filtering, information extraction, and Data Mining.
SY4: Document preparation and cleaning, feature extraction, and term weighting strategies.
SY5: Vector space models and similarity measures.
SY6: Introduction to statistical machine learning and evaluation measures.
SY7: Supervised learning: Naïve Bayes, KNN, and k-means.
SY8: Topic Modeling.
SY9: Resources for Text Mining.
SY10: Introduction to Deep Learning.
SY11: LLMs and Retrieval Augmented Generation (RAG) models."

"This course follows the semester-long assessment model (ALS).

The ALS consists of the following elements:
- 1 practical work [40%]
- 3 mini-tests [20% each * 3 = 60% in total]

The practical work can be done individually or in groups, consisting of the development of a project that will be subject to an individual oral discussion.

The practical work has a minimum mark of 9,5.

In case of failure in the ALS (<10 points), or if the student opts for Assessment by Exam, the exam corresponds to 100% of the grade."

LO1: Identify the analytical needs of the project (understanding the business).
LO2: Identify the variables that lead to the necessary knowledge.
LO3: Process the data using appropriate techniques and tools to achieve the proposed objectives.
LO4: Produce, in an appropriate document, the correct dissemination of the results obtained, explaining the decisions to implement the project.
LO5: Deal with the problem of data privacy, access and quality.
LO6: Develop critical thinking when exploring, analysing and communicating data for decision-making.

PC1 - Publicising and selecting projects applied to Integrated Decision Support Systems.
PC2 - Review of the methodology for developing Data Mining projects (CRISP-DM).
PC3 - Survey of the state of the art based on the PRISMA methodology.
PC4 - Project development using techniques and tools appropriate to each project.
PC5 - Templates for publicising the results obtained.
PC6 - Data privacy, access and quality issues.
PC7 - Writing a scientific article.

The course does not have the option of assessment by examination, given its nature as a project applied to real situations.
Assessment throughout the semester is made up of the following assessment components for the practical work/project:
Compulsory continuous assessment (no exam):
3 mid-term deliveries (group, with individual mark): 3 × 5% = 15%

Final report (article format, group, with individual mark): 50%
Presentation and discussion of the project (group, with individual mark): 35%
Additional notes:
Projects can be individual or in groups (max. 4 elements);
Minimum mark of 10 in the final report and presentation;
Mandatory attendance at the final presentation;

At the end of the curriculum period of this course, students will have the ability to:
LO1: Understand the disciplinary field of Social Psychology os Health.
LO2: Deepen knowledge of concepts and theories in Social Psychology of Health.
LO3: Comprehend the concepts of health and illness from the perspective of Social Psychology of Health.
LO4: Apply Social Psychology of Health to the use of technology.
LO5: Critically discuss the benefits and constraints associated with the use of technology.

Chapter 1:
PC1: Introduction to Social Psychology of Health
PC2: The disciplinary field of Social Psychology of Health
PC3: Stress, illness, and coping

Chapter2:
PC4: Health behaviors and illness behaviors
PC5: Explanatory theories of health behaviors
PC6: Quality of life, chronic illness, and hospitalization
PC7: Health promotion and disease prevention

Chapter 3:
PC8: Social Psychology of Health in the technological era
PC9: Technology as a tool in psychological interventions
PC10: Ethics and professional ethics in online interventions

Chapter 4:
PC11: New challenges for intervention

Evaluation throughout the period:

Individual class participation - weighted at 10%, requiring a minimum attendance of at least 2/3 of the classes.
Mid-semester exam - weighted at 25%, minimum grade of 8 out of 20.
Individual pitch at the end of the semester - weighted at 15%, minimum grade of 7.5 out of 20.
Group project, written text, and oral discussion - weighted at 50%, minimum grade of 8.5 out of 20.

The average of these four assessment components must be equal to or greater than 9.5 points.

Evaluation through an exam (available in Period 1 by student choice, Period 2, and Special Period) - weighted at 100%, minimum grade of 9.5 out of 20 or higher.

São objetivos de aprendizagem (OA) desta Unidade Curricular:
OA1. Conhecer as cadeias logísticas (abastecimento e circulação interna) das organizações de saúde
OA2. Conhecer as principais soluções digitais ao serviço da logística em saúde e os seus efeitos na capacidade de resposta aos cidadãos (sistemas de rastreamento de produtos/medicamentos; esterilização inteligente; data analytics, etc.)
OA3. Compreender o conceito de desperdício em saúde e o papel da logística digital na sua redução.

Esta UC detém os seguintes conteúdos programáticos (CPs):
CP1. Conceito de logística e de logística na saúde
CP2.Tecnologias digitais aplicadas à logística na saúde e exemplos de “smart hospitals”.
CP3. Qualidade em saúde e o papel da logística digital em saúde
CP4. Desperdício nas organizações de saúde

There are two possible forms of assessment: periodic evaluation and final exams.

Periodic evaluation:
- Elaboration of group work (30%);
- Presentation of group work (20%);
- Participation in class (10%);
- Written test (40%)

The minimum grade for validation of the evaluation in all components is 8 points.

Final exam: individual written test (100%)

The learning objectives (LO) of this Course Unit are:
LO1. Allow the student to have knowledge about telehealth, its concepts, definitions and applications.
LO2. Know the role of mobile technologies and issues related to portability in the context of Telehealth LO3. Allow the student to realize the relevance and challenges in the field of solutions for assisted health technologies;
LO4. Know the principles and know how to use intelligent sensor networks based on edge and cloud computing for the acquisition of physiological and environmental signals

This UC has the following contents (CPs):
CP1. Telehealth and Assistive Technologies for health: Concepts, definitions and typology;
CP2. Models of interaction in Telehealth (real time, deferred, etc.);
CP3. General architecture of an IoT system for health: biomedical sensors, acquisition and computing platforms, communication protocols for sensors and actuators, and information systems;
CP4. Conception and design of wearable devices in terms of usability and ergonomics;
CP5. Signal acquisition and processing
CP6. Case studies in telehealth and in assistive technologies for health

Resolution of 2 to 4 exercises/case analysis/individual challenges (R)
2 group assignments (TG1 + TG2) - Groups between 4-6 students.
1 oral presentation/individual written presentation (A)
Final grade = R*0.3 + TG1-2*0.5 + A*0.2
Students who were not successful in the periodic assessment (minimum 10 points) are submitted to a recourse exam worth 100% of the grade

To succeed in this course, students should be able to:
LO1. Develop and apply techniques for preparing and analysing exploratory data
LO2. Develop and apply computer vision, knowledge transfer and data augmentation techniques
LO3. Understand the application of image classification techniques such as Neural Networks and Convolutional Neural Networks.
LO4. Understand the application of classification techniques such as Decision Trees, Neural Networks and Bayesian Classification.
LO5. Apply rule induction techniques to knowledge extraction problems
LO6. Develop and apply time series techniques for analysing and forecasting data.
LO7. Determine assessments for good modelling.

PC1. Introduction to data mining, decision problems and applications
PC2. The data cycle process.
PC3. Review of descriptive and exploratory statistics.
PC4. Operations with images
PC5. Extraction of image characteristics
PC6. Classical neural networks
PC7. Convolutional neural networks
PC8. Knowledge transfer
PC9. Decision trees.
PC10. Bayesian inference techniques.
PC11. Time series

Assessment will take place throughout the semester, through two group assignments, each worth 30% of the final grade, and a written test worth 40%.
Each of the assessment components has a minimum mark of 8, and a final mark of at least 10 is required to pass the course.
The marks for the assignments may vary depending on the performance demonstrated individually in an oral discussion, which will take place (for the group) if the mark (of one of the members) between the test and the assignment has a difference of more than 3 points.
Given the practical nature of the course, there is no exam.
A minimum attendance of no less than 2/3 of the classes is required.
Grades can only be improved by repeating the assessment process the following year.

The learning objectives (LO) of this Course Unit are:
LO1. Know health policies priorities;
LO2. Relate state regulation and medical regulation in the configuration of health policies;
LO3. Understand that governance models in health need data and processes for digitization and dating;
LO4. Understand clinical management function;
LO5. List the operational challenges of healthcare organizations relevant in the contexts of prevention and healthcare provision;
LO6. Understand patient pathways through the transversality of a health organization such as a hospital.

This course includes the following programmatic contents (PCs):
PC1: Characterize and elaborate on the concept of a healthcare system (universal or non-universal).
PC2: Establish international comparisons of healthcare systems.
PC3: Understand the concept of health and the dimensions and functions of healthcare systems and their intersectoral nature.
PC4: Map the Portuguese healthcare system (legislation and framing documents of the Portuguese healthcare system).
PC5: Analyze the importance and impact of the digitization of the Portuguese healthcare system.
PC6: Familiarize with the concept of care integration based on the users' journey within the Portuguese healthcare system.
PC7: Deepen the relationship of the Portuguese healthcare system with the citizen users of healthcare services.

Two assessment modalities are planned: 1) Evaluation Throughout the Semester and 2) Exam Assessment.

1) Evaluation Throughout the Semester:

Assessment elements:
Class participation - 20% weight
Group project completion - 20% weight
Group project presentation - 20% weight
Final semester written test - 40% weight

To pass the curricular unit under the Evaluation Throughout the Semester modality, the following are required:
_Minimum attendance of at least two-thirds of the classes
_Minimum grade of 8 in each assessment element
_Final weighted average of the four assessment elements equal to or greater than 9.5

Exam Assessment: Completion of a written exam with a 100% weight and a minimum grade of 9.5.

After completing this UC, the student will be able to:

LO1. Identify the main themes and debates relating to the impact of digital technologies on contemporary societies;
LO2. Describe, explain and analyze these themes and debates in a reasoned manner;
LO3. Identify the implications of digital technological change in economic, social, cultural, environmental and scientific terms;
LO4. Predict some of the consequences and impacts on the social fabric resulting from the implementation of a digital technological solution;
LO5. Explore the boundaries between technological knowledge and knowledge of the social sciences;
LO6. Develop forms of interdisciplinary learning and critical thinking, debating with interlocutors from different scientific and social areas.

S1. The digital transformation as a new civilizational paradigm.
S2. The impact of digital technologies on the economy.
S3. The impacts of digital technologies on work.
S4. The impact of digital technologies on inequalities.
S5. The impacts of digital technologies on democracy.
S6. The impacts of digital technologies on art.
S7. The impacts of digital technologies on individual rights.
S8. The impacts of digital technologies on human relations.
S9. The impacts of digital technologies on the future of humanity.
S10. Responsible Artificial Intelligence.
S11. The impact of quantum computing on future technologies.
S12. The impact of digital technologies on geopolitics.

The assessment process includes the following elements:

A) Ongoing assessment throughout the semester
A1. Group debates on issues and problems related to each of the program contents. Each group will participate in three debates throughout the semester. The performance evaluation of each group per debate will account for 15% of each student's final grade within the group, resulting in a total of 3 x 15% = 45% of each student's final grade.
A2. Participation assessment accounting for 5% of each student's final grade.
A3. Final test covering part of the content from the group debates and part from the lectures given by the instructor, representing 50% of each student's final grade.
A minimum score of 9.5 out of 20 is required in each assessment and attendance at a minimum of 3/4 of the classes is mandatory.

B) Final exam assessment Individual written exam, representing 100% of the final grade.

"LO1: Define the main concepts, steps and methods involved in the development of Text Mining processes for the Healthcare sector.
LO2: Segment documents, create dictionaries, and perform other pre-processing tasks to prepare text for classification tasks relevant in healthcare.
LO3: Select and justify appropriate techniques for specific text processing tasks.
LO4: Construct vector representations from texts.
LO5: Explain the operation of algorithms for text classification, such as Naïve Bayes or KNN.
LO6: Apply a classifier to real cases.
LO7: Group documents using the k-means algorithm.
LO8: Develop prompt engineering in LLMs."

"SY1: Usefulness of large amounts of text, challenges and current methods in healthcare.
SY2: Unstructured vs. (semi-)structured information: Electronic health records, clinical notes and medical examination reports.
SY3: Information retrieval and filtering, information extraction, and Data Mining.
SY4: Document preparation and cleaning, feature extraction, and term weighting strategies.
SY5: Vector space models and similarity measures.
SY6: Introduction to statistical machine learning and evaluation measures.
SY7: Supervised learning: Naïve Bayes, KNN, and k-means.
SY8: Topic Modeling.
SY9: Resources for Text Mining.
SY10: Introduction to Deep Learning.
SY11: LLMs and Retrieval Augmented Generation (RAG) models."

"This course follows the semester-long assessment model (ALS).

The ALS consists of the following elements:
- 1 practical work [40%]
- 3 mini-tests [20% each * 3 = 60% in total]

The practical work can be done individually or in groups, consisting of the development of a project that will be subject to an individual oral discussion.

The practical work has a minimum mark of 9,5.

In case of failure in the ALS (<10 points), or if the student opts for Assessment by Exam, the exam corresponds to 100% of the grade."

LO1: Correct the user and/or organization problem identified in the Applied Project I course of the 1st semester, developing, in an iterative way, an integrated project with all its components, including requirements gathering, solution prototyping (lo-fi, hi-fi, MVP), and evaluation and field deployment of the innovative solution, regarding product, process or service (PPS).
LO2: Produce design documentation of the PPS innovation solution, including, where applicable, architecture, hardware and software configuration, installation, operation and usage manuals.
LO3: Produce solutions with the potential to be triple sustainable in the field, taking into account the applicable legal framework.
LO4: Produce audiovisual content on the achieved results, to be exploited in several communication channels: social networks, landing page web, presentation to relevant stakeholders, demonstration workshop.

PC1. Solution space: ideation of the best technological solution relative to the project, development of user requirements, storyboarding, user/costumer journey, iterative prototyping cycles (low fidelity - lo-fi, high fidelity - hi-fi, minimum viable product - MVP), heuristic evaluation of the solution with experts and evaluation with end users.
PC2. Production of solution design documentation, including, where applicable, architecture, technical specifications, hardware and software configuration, installation, operation and use manuals.
PC3. Experimental deployment of the solution with the potential to be triple sustainable (with economic, social and environmental value creation), safeguarding the applicable legal framework.
PC4. Audiovisual communication on the Web and social networks. Communication in public and its structure. Presentation to relevant actors.
PC5. Demonstration in workshop with relevant actors in the field of digital health technologies.

Assessment throughout the semester is made up of the following assessment components for the practical work/project:
- a mid-term paper presented in class (as a group, with an individual mark) - 10%
- final report in article format (in groups, with an individual mark) - 50%
- presentation and discussion of the project (in groups, with an individual mark): 20%
- poster presentation of the work (20%)
All group members must be present at all presentations.
Projects can be carried out individually or in groups (max. 4 members).
Minimum mark of 10 for the final report and the presentation and discussion of the project.
Project presentations are held with each group during the assessment period.

At the end of this course, the student should be able to:
LO1. Understand cybersecurity in its different perspectives
LO2. Understand the main security challenges and threats that organisations and users have to face;
LO3. Introduce the legal, ethical and strategic context of information security
LO4. Identify and manage information security risk;
LO5. Know and apply appropriate security technologies for risk mitigation;
LO6. Know mechanisms for the management and maintenance of information security environments.

SC1. Introduction to Cybersecurity: main components; cybersecurity pillars; cybersecurity frameworks.
SC2. Information Security Planning and Legal and Ethical Framework
SC3. Principles of Information Security Governance and Risk Management
SC4. Introduction to Information Security Technology: access controls, firewalls, vpns, idps, cryptography and other techniques.
SC5. Physical Security: physical access control mechanisms, physical security planning, among others.
SC6. Information Security Implementation: information security project management; technical and non-technical aspects of information security implementation.
SC7. Personnel Security: personnel security considerations; personnel security practices.
SC8. Maintenance of Information Security.

Assessment throughout the semester:
- Carrying out a set of group projects and activities (60%) throughout the semester.
- Two individual tests (40%) [minimum mark of 6 for each test].
Attendance at a minimum number of classes is not compulsory for the assessment throughout the semester.
Assessment by exam:
For students who opt for this process or for those who fail the periodic assessment process, with 3 seasons under the terms of the RGACC.