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: Understand the syntax of the Python programming language.
LO4: Develop programming solutions for problems of simple complexity.
LO5: Explain, execute, and debug code fragments developed in Python.
LO6: Interpret the results obtained from the execution of code developed in Python.
LO7: Develop programming projects.

S1. Introduction to Programming: Logical sequence and instructions, Input and output of data, Constants, variables, and data types, Logical, arithmetic, and relational operations, Control structures
S2. Procedures and Functions
S3. References and Parameters
S4. Integrated Development Environments
S5. Syntax of the programming language
S6. Objects and object classes
S7. Lists and Lists of Lists
S8. File Manipulation

The course unit follows a project-based assessment model due to its highly practical nature and does not include a final exam.
Students are evaluated based on the following parameters:
A1: Programming tasks validated by the instructors (10%), with a minimum grade of 9.5 out of 20 in the average of the tasks.
A2: Individual Project with theoretical-practical discussion (40%), with a minimum grade of 8.5 out of 20.
A3: Group Project with theoretical-practical discussion (50%), with a minimum grade of 8.5 out of 20.

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.

Approval with classification not less than 10 points (1-20 scale) in one of the following modalities:
- Periodic assessment: 3 mini-tests (MT) on classes of 30 minutes duration (MT1: 5%, MT2: 15%, MT3: 15%) + Test on the first examination period (40%) + weekly tasks on Moodle (15%) + work done in groups of 2-3 students (10%).
The average of the classifications of mini-tests 2 and 3 ( (MT2+MT3)/2 ) must be greater or equal to 7 points.
The classification in the final test must be greater or equal to 7 points.
There is the possibility of oral assessment.
or
- Assessment by Examination (100%), in any of the examination periods.

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%)

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

Periodic Assessment (only available in Period 1):
Individual Pitch (in 2 out of 4 chapters)
Pitch 1: 5% of the final grade
Pitch 2: 10% of the final grade
Minimum grade of 7.5 points
Two Individual Essays (half in-class/half take-home):
Essay 1: 15% of the final grade
Essay 2: 20% of the final grade
Minimum grade of 7.5 points
Group Work, written text, and oral discussion:
50% of the final grade
Minimum grade of 8.5 points
The average of these three 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).

LO1: Know the main theories, concepts and problematics 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 of the digital transition;
LO4: Explore cases, strategies and application methods to understand the real impacts of the digital transition on professions, companies and organizations.

S1. Is work different today than in the past? S2. How has theory looked at technology?
S3. What technologies for the future?
S4. What future for work?
S5. How intelligent is artificial intelligence?
S6. Where does precarity begin?
S7. Do platform workers need employment contracts?
S8. Who is to blame when the machine goes wrong?
S9. Are digital technologies changing the relationship between unions and companies?
S10. Does teleworking make people happier?
S11. Portugal and the digital transformation?

"Periodic evaluation:
Making of an Inverted class class. Each Inverted Class represents 20% of the final mark, with a minimum mark of 8. Weekly question and answer which represents 10% of the final mark, with a minimum mark of 8. An individual assignment, spread over 3 assessment periods, with a minimum mark of 8 in each, representing 35% of the final grade. A group assignment, representing a total of 35% (10% group presentation and 25% written assignment), with a minimum mark of 8. The average grade must be equal to or greater than 9.5.

Assessment by exam (First season 1 if the student chooses, Second Season and Special Season): In-person exam (100% of the final 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

Season 1: Periodic Assessment or Final Exam
Periodic Assessment:
-2 Tests (90%), with a theoretical and practical component. Minimum final average of 9.5, distributed as follows: (45%) Test 1 with a minimum score of 7.5 and (45%) Test 2 with a minimum score of 7.5
-(10%) Application and demonstration of knowledge tasks
Final Exam:
- (100%) Final Exam with a theoretical and practical component
Students have access to the Exam assessment in Season 1 if they choose it at the beginning of the semester or if they fail the Periodic Assessment.
Season 2: Final Exam
- (100%) Final Exam with a theoretical and practical component
Special Season: Final Exam
- (100%) Final Exam with a theoretical and practical component

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) by separating variables
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 and constrained extrema(otimization)
LG13 Articulate the various approaches to content, graphical, numerical and algebraic

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 Riemann definite integral. Fundamental theorem of calculus and antiderivatives
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 classification >=10 points (1-20 scale) in one of the following modalities:
-Continuous assessment: Test 1 (10%) + Test 2 (20%) + practical work in Python (10%) + autonomous work (10%) + Final Test (40%). The average of the 2 tests and the classification on the final test must be >=7 points (1-20 scale). In case of big differences in the classifications on tests and final test, an oral assessment might be necessary.
-Assessment by Exam (100%), in any of the exam periods

The objective of the UC is to develop a technological project in line with the scope of the Course. Contact will be established with project planning considering the main phases: Requirements analysis, development, partial tests and final tests and changes. Contact with laboratory equipment and tools is one of the goals for designing a software, hardware or both project.

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

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

The learning objectives (LO) of this Course Unit are:
OA1. Know health policies priorities; OA2. Relate state regulation and medical regulation in the configuration of health policies;
OA3. Understand that governance models in health need data and processes for digitization and dating; OA4. Understand clinical management function; OA5. List the operational challenges of healthcare organizations relevant in the contexts of prevention and healthcare provision. OA6. 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.

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

Learning Outcomes:
LO1. Develop oral communication skills
LO2. Improve body expression
LO3. Master the art of using the vocal apparatus
LO4. Learn performance techniques

Compatibility with the Teaching Method:
The course combines theory and practice, providing students with an immersive experience in the world of public performances with theatrical techniques. The teaching method is interactive and participatory, encouraging students to put into practice the concepts learned through individual and group exercises.

The knowledge acquired involves both theatrical theory and specific oral communication techniques. Participants will learn about the fundamentals of vocal expression, character interpretation and improvisation, adapting these skills to the context of public presentations

S1 - Preparation for presentation (3 hours)
S2 - Non verbal communication (3 hours)
S3 - Introduction to using the vocal apparatus (3 hours)
S4 - Introduction to the term Performance (3 hours)

Modality of continuous assessment:
Practical Presentations (50%): Participants will be assessed based on their public presentations during the course. Criteria such as clarity of communication, vocal and body expression, use of theatrical techniques and performance will be considered. Presentations may be individual or group presentations, depending on the activities proposed.

Exercises and Written Assignments (50%): In addition to the practical presentations, participants may be asked to complete exercises and written assignments related to the content covered in each module. These may include reflections on learned techniques, analysis of case studies, answers to theoretical questions or even the creation of presentation scripts. These activities will help to assess participants' conceptual understanding.

To conclude the curricular unit in the modality of continuous assessment the student must be present in 75% of the classes.
Although not recommended, students may opt for final assessment by written and oral examination (100%).

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)

Modality of continuous assessment:
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, 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, quality of the solutions and collaboration among group members.

To conclude the curricular unit in the modality of continuous assessment the student must be present in 75% of the classes.
Although not recommended, students may opt for final assessment by written and oral examination (100%).

"LO1. Knowledge about the structure, language, ethical and normative procedures for the elaboration of academic texts.
LO2. Skills to use generative algorithms to assist the elaboration of academic work.
LO3. Skills in analysing and scrutinising the independence, relevance and reliability of AI generated data.
LO4. Overall abilities to recognise the ethical and civic implications underlying the access, sharing and use of AI tools in an academic context."

"S1. Introduction to Academic Writing and generative algorithms (3h)
S2. Procedures for planning and constructing argumentative texts with the aid of AI (3h)
S3. Critical analysis of texts produced: identification and referencing of data sources and analysis of their relevance in the ligth of the objectives of the academic work (3h)
S4. Opportunities and risks of AI use: good practice guide for accessing, sharing and using AI tools in an academic context (3h)"

"Modality of continuous assessment:
Class participation: Class participation: assesses students' attendance, involvement and individual contributions to class discussions and activities (20%).
Group work will require students to form groups to revise and edit academic texts between themselves, using generative algorithms. Assessment will be based on the quality of the revisions, edits and feedback provided (40%).
Individual report: with an in-depth reflection on the civic and ethical questions posed by the use of AI tools as an aid to academic writing (40%).
There is a required minimum of 7 values in each component that is graded.

To conclude the curricular unit in the modality of continuous assessment the student must be present in, at least, 75% of the classes.
Although not recommended, students may opt for final assessment by written and oral examination (100%).

In addition to the practical presentations, students will be asked to carry out exercises and written tasks related to the content covered. These may include: reflecting on techniques learnt, analysing case studies, answering theoretical questions or even creating presentation scripts. These activities will help to assess conceptual understanding of the content taught.

LO1 Know the basic principles of Information Systems and their role in organizations
LO2 Know the fundamental concepts of Information Systems Analysis and develop semantic (conceptual) models for systems described in text, through practical application of the UML language, and understand the conversion of such conceptual models into relational database models (RDBs)
LO3 Know how to model and design a Relational DB (RDB), with the Relational Model
LO4 Know the normal forms and relational algebra and understand the normalization of an existing RDB based on performance metrics
LO5 Know how to create and modify the physical structure of a RDB using SQL
LO6 Know how to use, at an elementary level, the administration tools associated with a Database Management System (DBMS)
LO7 Develop self-learning, peer review, teamwork, oral and written expression

S1 Introduction to Information Systems and its role in organizations
S2 Introduction to Information Systems Analysis with UML language: requirements analysis, data models, schemas and UML diagrams
S3 Database Design. Relational Model: relationships, attributes, primary keys, foreign keys, integrity rules, optimizations and indexes
S4 Normalization. Redundancy and inconsistency of data. Normal forms
S5 SQL Language - Table variables, set operators, simple queries, subqueries, operators (SELECT, Insert, delete, update), views, indexes, triggers, stored procedures and transactions
S6 Introduction to Database Management Systems administration, DBMS

Periodical Assessment:
- 1 test to be done in the middle of the semester (30%)
- 1 test to be taken in the 1st season of exams (30%)
- 1 modelling and implementation project (40%)

Both tests have a minimum grade of 8 values and the project is mandatory for approval.

Assessment by exam:
-1 Written exam weighted at 100%

The minimum grade for approval in this course is 10 values.

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%.

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, t and F distributions
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, t and F distributions.
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:
- Periodic Assessment: 2 mini-tests (MT) taken in class (15% each) + Final test taken on the date of the first exam (40%) + autonomous work (10%) + group project (20%),
The average of the mini-tests ( (MT1+MT2)/2 ) has a minimum mark of 7.0.
The final test has a minimum mark of 7.0.
or
- Assessment by Exam (100%).

After successfully attending the curricular unit, students should be able to:

OA1. Know the different data formats.
OA2. Know the complete data cycle.
OA3. Know how to perform exploratory data analysis using R.
OA4. Know how to model a set of data.
OA5. Implement a data analysis solution for a given problem.

CP1. Introduction to Data Analysis
CP2. Introduction to R and RStudio
CP3. Knowledge of problems in data analysis with examples
CP4. The complete cycle of data analysis
CP5. Data and data format
CP6. Data preparation
CP7. Odds; Descriptive Statistics and Exploratory Analysis
CP8. Data visualization
CP9. Modeling and different types of machine learning problems
CP10. Model evaluation methods
CP11. Reporting and publishing results

PERIODIC assessment results from: online exercises, without a minimum grade, after each class (20%); two individual tests - a mid-term test and another at the end of the semester (30%); and a group work (maximum of 3 students) in R with preparation of a report and oral presentation (50%).

Students who obtain a final grade above 9.5 are approved.

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: periodic assessment and final exam.

Periodic Assessment:

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

Final Exam: Individual written exam (100%).

The learning objectives (LO) of this Curricular Unit are to enable students to acquire the following competencies:
LO1. Understand the relevance of data in hospital environment for management purposes;
LO2. Know which are the hospital equipments that provide relevant data for hospital management;
LO3. Know what are the challenges related to the management and maintenance of hospital equipment;
LO4. Understand which are the applications and added values of the data taken from the equipment;
LO5. Know what are the challenges related to cybersecurity of equipment and data, as well as the importance of its integrity;
LOA6. Know the principles of Data Science and data-driven decision making.

This CU has the following programmatic contents (CPs):
CP1. Optimized management guided by data;
CP2. Types of spend metrics to monitor
CP3. Equipment management and problems associated with maintenance;
CP4. Types of data gathered by hospital equipment and its applications in a more efficient and optimized management;
CP5. Cybersecurity of equipment and data, as well as the problems of ethics and privacy associated with it;
CP6. Online availability and integrity of databases.

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.

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.

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.

Periodic Assessment:
- Realisation of a set of group projects and activities (60%) throughout the semester
- Two individual tests (40%) [minimum score of 6 points for each test].
Attendance of a minimum number of classes is not compulsory in Periodic assessment.
Assessment by examination:
For students who opt for this process or for those who fail the periodic assessment process, with 3 epochs under the RGACC.

The aim is to introduce the concepts and practical knowledge fundamental to the design and implementation of an analytical information system for an organization:
(LO1) Plan and manage the life cycle of a data warehouse project, logical and physical design;
(LO2) Identify requirements and data sources and design an appropriate dimensional model;
(LO3) Model an Analytical Information System;
(LO4) Design and implement a data extraction, transformation and loading process;
(LO5) Analyze data in a data warehousing system , have an understanding of Business Intelligence and its applicability; what are standard reports and performance indicators ( KPIs );

(CP1) SQL review: simple queries. Union of tables. Ordering. Grouping and aggregation.
(CP2) Data integration, introduction to ETL tools: data sources. Data integration using visualizations. Data integration versus data warehousing. ETL process. ETL tools.
warehouse design : OLAP operations in SQL. Multidimensional model. Modeling in UML. Typical OLAP operations on a data cube. Data warehouse schema types . Hierarchies and types of hierarchy. Measurements.
(CP4) The OLAP Cube and MDX queries - Data storage and OLAP cubes. OLAP server. OLAP cube definition. OLAP interface. Analysis queries. SQL versus MDX. MDX Concepts.
(CP5) Reporting tools and ( KPIs ) - Data warehousing architecture overview . OLAP tools: front-end and MDX queries . Reporting tools. Reports via SQL and MDX queries. Key performance indicators ( KPIs ). Visualization with dashboards .

Periodic assessment results from the following components:

- A midterm test (20% of the final grade) and another at the end of the semester (20% of the final grade);

- Group work (maximum 3 students) in which the group will develop an analytical information system with the writing of a report which involves 3 deliverables to be submitted along the semester(30% of the final grade, 10% each deliverable) and an oral presentation with a demonstration of the operation of the developed application and discussion (30% of the final grade).

Alternatively, students may choose to be assessed in a final exam (100% of the final grade)

Students who obtain a final grade above 9.5 are approved.

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.

LO1: Knowing the definition and fields of AI and Machine Learning
LO2: Knowing the definition of intelligent agent and its characteristics
LO3: Identifying the components of an Intelligent Tutor System (ITS)
LO4: Applying the best knowledge representation in ITS
LO5: Applying the best inference and diagnosis methods in distinct contexts
LO6: Applying conversation and recognition methods for pacient-computer interaction
LO7: Applying forms of Interaction Human - AI for collaboration and healthcare monitoring
LO8: Identifying methods for customizing and adapting healthcare models
LO9: Applying IA techniques for diagnosis
LO10: Identifying the principles of a responsible and reliable AI

S1: Introduction to Artificial Intelligence and Machine Learning
S2: Introduction to Intelligent Agents
S3: Intelligent Tutoring Systems
S3.1: ITS Components
S3.2: Patient Modeling
S3.3: Knowledge Representation and Inference
S4: Conversation Agents and Virtual Tutors
S5: Human-AI Collaboration
S6: Intelligent Content Generation and Management
S7: Analysis of Healthcare Data
S8: AI in Education - Ethical and Responsible

The course does not include assessment by exam. Students are evaluated at 100% through project:
Exam Period 1: A1. Programming tasks validated by professors (20%), minimum grade of 9.5 A2. Individual Project with discussion (35%), minimum grade of 8.5 A3. Group Project with discussion (45%), minimum grade of 8.5
Exam Period 2: A1. Individual Project with discussion (100%), minimum grade of 9.5
Extraordinary Period: A1: Individual Project with discussion (100%), minimum grade of 9.5

At the end of the course, the student should be able to:
LO1: Apply co-creation methodologies in the development of innovative triple sustainable projects (with economic, social and environmental value) in organizations.
LO2: Create empathy with the user and his organization (define needs, obstacles, goals, opportunities, current and desired tasks), define the problem and raise the issues addressed by the project.
LO3: Conduct a systematic literature review and competitive landscape analysis (if applicable), related to the identified problem and the issues raised.
LO4: Identify the digital (including data collection), computational and other resources needed to address the problem.
LO5: Apply already consolidated knowledge of project planning, agile management and project development, within the framework of group work.
LO6: Participate in collaborative and co-creation dynamics and make written and oral presentations, in the context of group work.

S1 Co-creation methodologies based on Design Thinking and Design Sprint
C2 Sustainable Development Goals (SDGs) of the United Nations. Creation of value propositions
S3 Presentation of case studies and digital technologies project topics of health (product, service or process)
S4 Selecting the project topic and framing it in the organization
S5 Problem space: creating empathy with the user and his organization, defining the problem and its related issues, considering business requirements, customer and user needs, and technology challenges
S6 Application of a systematic literature review methodology and its critical analysis. Competition analysis (if applicable)
S7 Identification of digital resources (including data collection), computational, and other resources required for project development
S8 Application of agile project management methodologies, appropriate to the group work to be developed by the students of health. Communication of results.

Course in periodic assessment, not contemplating final exam, given the adoption of the project-based teaching-learning method applied to real situations. Presentations, demonstrations and discussion will be carried out in groups.
Assessment weights:
R1 Report: Project Topic Definition: 5%.
R2 Report: Empathy with the User and the Organization and Definition of the Problem. Its presentation and group discussion: 40%
R3 Report: Systematic Literature Review and Project Development Planning. Its presentation and group discussion: 55%.

The student who successfully completes this UC will be able to:
OA1. Identify the main contemporary issues and debates;
OA2. Analyze current issues and debates in a reasoned manner;
OA3. Identify the implications of technological change and digitalization in economic, social, cultural and environmental terms;
OA4. Understand the role and the importance of technology in the challenges of contemporary societies;
OA5. Explore the boundaries between technological knowledge and social science knowledge;
OA6. Develop forms of interdisciplinary learning and critical thinking.

S1. Debates XXI: technological change and contemporary societal challenges.
S2. Digital transition: meaning and implications.
S3. Technology, social change and inequalities.
S4. Environment and transition towards to sustainability.
S5. Globalization, financialisation and development.
S6. Capitalism and democracy.
S7. Migrations and multiculturality.

The periodic assessment process comprises the following elements:
1. Preparation and presentation (class) of a group work on technological change and society (40%).
2. Test (60%).
The final assessment corresponds to 1st and 2nd phase exams (100% of the grade).

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%)

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.

S1. Solution space: ideation of the best technological solution relative to the project, development of user requirements, storyboarding, user/costumer journey, iterative prototyping cycles (low fidelity - lo-fi, high fidelity - hi-fi, minimum viable product - MVP), heuristic evaluation of the solution with experts and evaluation with end users.
S2. Production of solution design documentation, including, where applicable, architecture, technical specifications, hardware and software configuration, installation, operation and use manuals.
S3. Experimental deployment of the solution with the potential to be triple sustainable (with economic, social and environmental value creation), safeguarding the applicable legal framework.
S4. Audiovisual communication on the Web and social networks. Communication in public and its structure. Presentation to relevant actors.
C5. Demonstration in workshop with relevant actors in the field of digital health technologies.

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

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