LO1: Understand the historical context of Computing and HCI (Human-Computer Interaction) and the principles of user-centred Design
LO2: Understand the fundamental perceptive and cognitive characteristics of human beings and their respective limitations that impact HCI design.
LO3: Create empathy with the user (needs, goals, current and desired tasks, problems). Requirements based on collected data.
LO4: Apply principles and 'golden rules' of HCI design and usability in practical cases
LO5: Apply techniques/rules of visual screen design (WWW and mobility). Create storyboards and low-fidelity (Lo-Fi) and high-fidelity (Hi-Fi) prototypes. Perform ideation and development of the Minimum Viable Product (and its Lo-Fi).
LO6: Design and apply heuristic evaluation with Lo-Fi experts, leading to a new iteration and Hi-Fi development.
LO7: Design experimental studies of the Hi-Fi with end-users and apply usability and task satisfaction metrics based on collected data.

S1: Introduction, Program, and Assessment. Computing and HCI: History, state-of-the-art, and applications
S2: User-centered design process. We, the Humans
S3: User and task analysis. Empathy map. Personas. User "as is" scenarios and journeys. User questions. User requirements
S4: Principles and golden rules of interface design. Usability
S5: Visual design of screens (WWW, mobility)
S6: Ideation. Storyboards. Prioritization. Low-fidelity (Lo-Fi) and high-fidelity (Hi-Fi) prototypes of the solution
S7: Deliver a functioning solution. Heuristic evaluation with experts. User evaluation. Statistical analysis of evaluation data. Calculate metrics and iterate the design. Requirements of an MVP (Minimum Viable Product). Elevator Pitch to investors and users

Course under Periodic Assessment, not including a Final Exam. Weights:
70% Group lab project work + presentation and discussion.
30% Two multiple-choice mini-tests
In the second Exam period, mini-tests with a score of 7.5 or lower must be retaken. If the student fails the regular period (score < 10), is eligible to take the exam in the 2nd or special seasons (which counts for 30% of the grade). Need to pass the group project or an equivalent individual project (70% of the grade) is mandatory

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.

LO1: Know the basic principles of operation of a computer system;
LO2: Present the principles of hardware and software and indicate their combination in a computer;
LO3: Recognise the components and typical architectures of computers;
LO4: To know the structure, functions and operation of an Operating System (OS);
LO5: To know the different types of operating systems and their intrinsic characteristics;
LO6: To know the mechanisms of virtualization of systems.

S1: Introduction to numbering bases and base 2,8,16 codes; Calculations in various numbering bases; Encoding and representation of information (ASCII code and others).
S2: Computer structure: Motherboard, CPU, memories, stack, bus, storage system, graphics cards, communication ports, peripherals.
S3: Operating System Components: Processes; Memory; Stack; Input and Output; File System; Administration and Security.
S4: Study of Operating System commands: Linux and Windows.
S5: Virtualization environments: VMware; VirtualBox; Proxmox, others.
S6: Creation and use of virtual machines: Linux (Ubuntu, Fedora, CentOS, others) for workstations, for servers (e-mail, VoIP, storage); Windows (11, server); Networking of virtual machines.

Course with Periodic Assessment, not by Final Exam. Presence required in 90% of all the activities.. Assessment weights:
Individual practical assignments, 80% of which are compulsory (25%)
Lab project (in group of 4), with individual oral discussion (50%)
2 multiple response Mini-tests (25%)
Failing the regular season (< 10 val): able to access 2nd season exam (50% of the grade), replacing the individual works and mini-tests, being compulsory the approval in the group lab project (50%).

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

On completion of this course, students will be able to:
LO1. Know the basic operating principles of a computer network
LO2. Know and understand the OSI and TCP/IP reference models
LO3. Know how the main protocols used in everyday life work, particularly HTTP
LO4. Know and understand how protocols work at transport level.
LO5. Know how to interconnect devices in a wired network.
LO6. Be able to design, configure and manage a computer network

CP1. Introduction to computer networks and presentation of the OSI and TCP/IP reference models
CP2. Introduction to the physical and data link layer. Installation and configuration of a switch
CP3. Introduction to the network layer: IPv4 and IPv6 addressing; IPv4 protocol and creating subnets.
CP4. Packet forwarding; Operation and configuration of a router.
CP5. Exploring TCP/UDP transport protocols. Congestion control.
CP6. Exploring the application layer: DNS, E-mail and File Transfer.
CP7. Configuring firewalls
CP8. Computer network management

It can be accomplished in one of the following ways:

1. Periodic assessment:

Theoretical component
-1st test to be taken in the middle of the semester (30%);
-2nd test to be taken at the end of the semester (30%).
(there is also the possibility of doing a final exam (60%) for those who have failed the 1st and/or 2nd tests)

Practical component
-3 laboratories to be carried out in groups (15%);
-1 practical group assignment and its presentation (25%).

Note: Both tests and laboratories have a minimum mark of 8 values, and it should be noted that the practical component is mandatory for the purposes of approval in the periodic assessment. The minimum mark for passing the course is 10 values.


2. Exam evaluation:
- It is available in the first or second seasons
- Written exam (100%)
The minimum mark for passing the course is 10 values.

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 exibithion: 40%; final report: 30%. The presentations, demonstrations and defence are in group.

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.

LO1 Agile Basics: Understand Agile principles, including the Agile Manifesto, and differentiate it from Waterfall
LO2 Product vs Project: Grasp the nuances between Product and Project Management, focusing on Product Discovery and Delivery
LO3 Teamwork: Learn team collaboration through group projects, covering challenge mechanics and use-case proposals
LO4 MVP & Prioritization: Acquire skills in defining MVPs, mapping User Stories, and prioritizing features with techniques like Impact vs Effort and MoSCoW
LO5 Scrum & Kanban: Understand Scrum and Kanban methodologies, including Scrum rituals and artefacts like Backlogs, Epics, User Story Mapping, and Acceptance Criteria. LO6: Familiarize with tools like Azure Boards, and Google Analytics, and apply metrics like the AARRR funnel for product evaluation
LO7 Conduct retrospectives, gather user feedback and apply lessons learned for ongoing product improvement

S1 Fundamentals and Agile Manifesto. Agile versus Waterfall
S2 Product Management vs Project Management. Two-Way Development: Product Discovery & Product Delivery
S3 Group project challenge. Challenge mechanics. Use-case proposals and outcomes
S4 Agile digital product teams
S5 Introduction to Minimum Viable Product - MVP and User Story Mapping (USM)
S6 Agile Methodologies Scrum and Kanban. Scrum rituals and artefacts: Product and Sprint Backlog, Epics, USM, Acceptance Criteria
S7 Definition of MVP for each use case and respective USM, using Miro: requirements and features of each step
S8 Prioritizing features: Impact vs Effort Matrix and MoSCoW
S9 Agile product development planning using Azure Boards: MVP delivery in weekly sprints
S10 Metrics to evaluate product effectiveness and efficiency. AARRR Funnel. Analysis in Google Analytics. User interviews. Retrospective and lessons learned. Demo day.

Course w/ periodic assessment. No Final Exam. Assessment weights:
• 70% Lab project carried out in a group + the final presentation and individual discussion.
• 30% 3 Mini-tests with multiple choice and um final test
A mark below 10 assigns the student to an exam in normal and/or the appeal period (30% of the mark), where the completion and approval of the group project or an individual project (70%) is mandatory.

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%).

LO1 Structure the students' logical thinking in order to solve programming problems.
LO2 Empower students with the ability to perceive the object-oriented programming paradigm.
LO3 Use an object-oriented programming language and the necessary tools, to design, develop, test and debug small applications.
LO4 Understand and apply the concepts of abstraction, encapsulation, inheritance and polymorphism.
LO5 Know how to use the fundamental data structures of a library (stacks, queues, trees, scatter tables).
LO6 Apply error control mechanisms.
LO7 Explain the usefulness of using software design patterns and demonstrate their use in simple cases.
LO8 Develop creativity, technological innovation and critical thinking.
LO9 Develop self-learning, peer review, teamwork, and oral expression.

S1 Classes and Objects
S2 Inheritance and polymorphism
S3 Abstract classes
S4 Interfaces and comparators
S5 Collections: lists, sets, maps
S6 Anonymous classes and lambdas
S7 Reading and writing files
S8 Exceptions and error handling
S9 Genericity and design patterns
S10 JUnit Tests and Documentation

CU with Periodic Assessment, not including a Final Exam:
8 individual assignments (10%), min grade of 9.5
Group project, with oral discussion (45%), min grade of 9.5
2 Mini-tests (45%), minimum grade of 9.5
If failing in the first period (< 10 out of 20), the student can retake the 1st and/or 2nd mini-tests (can also retake if scoring below the minimum grade in either or both) - accounting for 55% of the grade, with passing the Project or completing an individual project being mandatory - 45%

LO1 Understand the concepts related to distributed and cloud computing
LO2 Develop a holistic and broad view about the organization and functioning of the existing computational models
LO3 Understand the dynamics of data generation e the need to process and retrieve value from them
LO4 Understand the principles that allow the creation of applications as well as services
LO5 Understand the mechanisms, technologies and protocols involved in the cloud and how they support its function
LO6 Develop creativity, technological innovation and critical thinking
LO7 Develop self-learning, peer revision, teamwork, and oral expression

C1 Introduction to cloud computing. Objectives, distribution models (SaaS, PaaS, IaaS, DBaaS), deployment and infrastructure. Distributed systems? concepts and concurrency. Introduction to cloud security. Redundancy and fault tolerance.
C2 Middleware using web services. Service-oriented architectures. The relationship between SOA and cloud computing. Web, HTTP protocol and RESTful architectural style. Services and communication between services. Technologies and web protocols. Middleware for the cloud.
C3 Distributed processing of large volumes of data. Data organization principles. Brief review on storage models. DaaS and NoSQL. MapReduce programming model.
C4 Developing applications for the Cloud. Integration heterogeneous sources of information. Geographic information distribution, Web Map Service (WMS) and GeoJSON

Course with Periodic Assessment, not by Final Exam. Presence is required in 90% of all the activities.
Assessment weights:
- Individual practical assignments, 80% of which are compulsory (25%)
- Lab project (in group of 2), with individual oral discussion (50%)
- 2 multiple response Mini-tests (25%)
A mark below 10 assigns the student to an exam in normal and/or the appeal period (50% of the mark), with the completion and approval of the group project, or an individual project mandatory (50%).

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.

LO1 Frame and understand the main components of the World Wide Web;
LO2 Know and correctly apply the client programming model and the MVC paradigm;
LO3 Use and extend server technologies to develop web applications and services;
LO4 Integrate web applications and services with Database Management Systems;
LO5 Understand the life cycle pipeline of a web project;
LO6 Develop creativity, technological innovation, critical thinking;
LO7 Develop self-learning, peer review, teamwork, oral expression.

S1 Introduction. The history of the Web. Programming languages for the Web; W3C standards.
S2 World Wide Web Architecture. Screen marking with HyperText Markup Language (HTML).
S3 Client-side programming. Structure description (HTML), style sheets (CSS) and dynamic updating of the graphical interface. Input validation; Introduction to client-side security.
S4 Server-side programming. Distribution of static content, dynamic generation of content and MVC design pattern. Services and communication between services. Introduction to server-side security.
S5 Data persistence. Integration with Database Management Systems
S6 Service-oriented web architectures. Web Services and Microservices. Middleware models for the Web. Containerization.

Course with Periodic Assessment, not by Final Exam.
Assessment weights:
- Lab project (in group between 2 and 4), with technical report, individual oral discussion (60%)
- 4 multiple response individual Mini-tests (40%)

A mark below 8 assigns (in average of mini-tests) the student to an exam in normal and/or the appeal period (40% of the mark) in a written test, with the completion and approval of the group project, or an individual project (with technical report and individual oral discussion) is mandatory (60%).

LO1 Know the main features and functionalities of the Python programming language
LO2 Know how to run and debug Python applications
LO3 Understand data science principles and the Cross-Industry Standard Process for Data Mining (CRISP-DM) model
LO4 Characterize the main families of algorithms used in Machine Learning
LO5 Know how to collect and prepare data for modeling
LO6 Understand and explain the fundamentals of supervised, unsupervised and reinforcement learning
LO7 Design, develop and test automatic learning algorithmic techniques in Python to solve real practical problems
LO8 Develop self-learning, peer review, teamwork, verbal and oral expression

S1 Introduction to programming language syntax and structure (Python 3)
S2 Integrated Python development environments. Program execution and debugging
S3 Control primitives, variables, expressions and declarations. Objects and object classes.
S4 Functions, modules and packages.
S5 Operations on files. JSON, XML data interpretation.
S6 Database operations. Web scrapping
S7 Introduction to data science. Discussion of problems and case studies. Data cycle and data mining
S8 Types of machine learning
S9 Model the Cross-Industry Standard Process for Data Mining (CRISP-DM)
S10 Data collection and preparation. Evaluating Results
S11 Unsupervised Learning
S12 Supervised Learning
S13 Reinforcement learning

Presence is required in 90% of all the activities of the course. Assessment weights:
- Individual practical work, 8 of which are compulsory (40%).
- Project (in groups of 2), with oral discussion (35%).
- 25% - Mini-tests.
A mark of 10 or higher exempts exam. A mark below assigns the student to an exam in the appeal period.

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.

At the end of the course students should be able to
OA1 Understand and identify the problems associated with the processing of large amounts of data and information
LO2 Understand the concepts and ecosystem of Big Data
LO3 Design and implement solutions for data storage in a distributed and fault tolerant environment
LO4 Extract, transform and load large amounts of information from unstructured data sources
LO5 Know how to manipulate and process non-relational databases
LO6 Understand and now how to apply distributed programming and computing models
LO7 Understand and now how to apply techniques for processing JSON structures and real time data streams
LO8 Develop creativity, technological innovation, critical thinking
LO9 Develop self-learning, peer review, teamwork, verbal and oral expression

S1 The concept of Big Data, applicable problems and its ecosystem
S2 Introduction to non-relational databases and MongoDB
S3 Computing architecture for Big Data: (1) redundant and fault tolerant and (2) distributed to support large volumes of data. Example of the Hadoop platform and its distributed file system
S4 The MapReduce programming model
S5 Designing databases in MongoDB
S6 Manipulation of JSON structures and real-time data
S7 The ETL - Extract, Transform and Load process aplied to unnormalized data sets and development of Big Data processing applications in Spark and MongoDB environments

Course with Periodic Assessment, not by Final Exam. Presence is required in 90% of all activities.. Assessment weights:
- Individual practical assignments, 80% of which are compulsory (25%)
- Lab project (in group of 2), with individual oral discussion (50%)
- 2 multiple response Mini-tests (25%)
A mark below 10 assigns the student to an exam in the normal and/or the appeal period (50% of the mark), with the completion and approval of the group project, or an individual project mandatory (50%).

At the end of the learning unit, the student must be able to:
LO1 Understand the mobile application development context, its characteristics and limitations
LO2 Identify the major mobile application development platforms and understand their features and differences
LO3 Plan a mobile application development project
LO4 Understand the differences between native mobile application development and mobile web development
LO5 Design, develop and test mobile applications in the different studied platforms
LO6 Apply the acquired knowledge in the development of a mobile application project on the selected platform

S1 Introduction to mobile application development
S2 Mobile devices features and limitations
S3 Native mobile application development platforms: Google Android, Apple iOS
S4 Web application development for mobile devices with HTML5, CSS3, JS
S5 Hybrid mobile applications development (ionic, ReactNative, Xamarin, Flutter)
S6 Mobile application project planning, design and development

Course with Periodic Assessment, not by Final Exam. Presence is required in 90% of all activities.. Assessment weights:
- Individual practical assignments, 80% of which are compulsory (25%)
- Lab project (in group of 2), with individual oral discussion (50%)
- 2 multiple response Mini-tests (25%)
A mark below 10 assigns the student to an exam in normal and/or the appeal period (50% of the mark), with the completion and approval of the group project, or an individual project is mandatory (50%).

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 in Software and Applications (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 in Software and Applications area. 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%.

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.

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.
S5. Demonstration in workshop with relevant actors in the field of software and applications.

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 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).