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

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

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

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

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

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

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

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.
S3: User and task analysis. Empathy map. Personas. User "as is" scenarios and journeys. User questions. User requirements.
S4: We, the Humans
S5: Principles and golden rules of interface design. Usability
S6: Visual design of screens (WWW, mobility)
S7: Ideation. Storyboards. Prioritization. Low-fidelity (Lo-Fi) and high-fidelity (Hi-Fi) prototypes of the solution
S8: 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

Assessment throughout the semester, not including a Final Exam. Weights:
70% Group lab project work + presentation and discussion
30% Two multiple choice mini-tests
Mandatory minimum attendance in 2/3 of classes
In the second Exam period, mini-tests with a score of ≤7.5 must be retaken. If the student fails the regular period (score <10), is eligible to take the exam in the 2nd or special seasons (30% of the grade). Is mandatory to pass the group project or an equivalent individual project (70%)

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

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

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

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

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

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

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

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.

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

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

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

Or

- Assessment by exam (100%).

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

Upon completing this course unit, students should be able to:

LO1. Understand the model proposed by the von Neumann architecture.

LO2. Recognise the relationship between software and hardware.

LO3. Understand how the basic components of a CPU interact and grasp the fundamental concepts of GPU architecture.

LO4. Comprehend the interaction between the CPU, GPU, and the memory and peripheral subsystems.

LO5. Understand the memory hierarchy and its impact on performance.

LO6. Comprehend key performance metrics and basic performance analysis techniques.

S1 Historical evolution of computers, human-computer interaction, impact on modern computing; von Neumann architecture: model, characteristics, limitations; alternatives.
S2 Binary representation, data types, instruction and program storage; code translation: programming languages, compilers, assembly.
S3 Instruction types and formats, addressing modes, memory access, execution cycle; control flow, data transfer.
S4 CPU, GPU, and Peripherals: CPU structure and components (ALU, CU, registers), internal organisation, data and control flow, pipeline concepts; introduction to GPU; motherboard, communication ports, peripherals.
S5 CPU-memory interaction, storage hierarchy, cache (mapping, replacement), main memory, persistent storage (HDDs, SSDs), virtual memory.
S6 Performance metrics (CPI, MIPS, FLOPS), factors influencing performance, analysis and optimisation of memory and processor usage, role of the operating system.

The assessment can be carried out in one of the following modes:

Assessment throughout the semester:

Two written tests during the semester, with a minimum grade of 8 out of 20 for each test (30% + 30%).

Group practical project and its oral presentation (40%).

Once the practical group work has been completed, if the student does not obtain the minimum grade in the written assessment tests, they may take the theoretical component assessment in an exam worth 60%.

Final Exam Assessment:

Written exam (100%).

To pass the course unit, students must achieve a final grade of 9.5 or higher out of 20.

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

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

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

Individual work accounting for 35% of the final grade.

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

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

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

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

LO1: Know the basic operating principles of a computer system
LO2: Make a clear distinction between hardware and software and explain how they interact
LO3: Identify the main physical components of a computer and describe their functions
LO4: Understand and describe different computer architectures
LO5: Understand how a computer runs programs and how it communicates with other computers and users
LO6: Know the components of operating systems (OS), describe their functions and how they are implemented in different OSs
LO7: Distinguish between different types of OS and their practical applications
LO8: Use the command line, scripts and the OS graphical environment to carry out administration tasks
LO9: Understand hardware and OS virtualization and their relevance to saving resources
LO10: Know how to implement hardware virtualization in type II hypervisors and in the cloud, and OS components in the Docker environment

PC1: Introduction to number bases 2, 8, and 16; binary addition and subtraction; coding and representation of information (ASCII and others).
PC2: Computer structure: system board; CPU (processor architecture); memories; BUS; storage system; graphics cards; communication ports; peripherals.
PC3: Components of Operating Systems (OS): Process management; Memory hierarchy; Input and output management; File system; Administration and security.
PC4: Study of Linux and Windows OS commands.
PC5: Hypervisors type II (VMware, VirtualBox) - Create, OS configuration, export and import of virtual machines (VMs) Windows (client and server), Linux (client, firewall and email servers, VoIP, storage); Networking VMs.
PC6: Clouds (Azure and others)—Create Windows and Linux VMs; access and use VMs in clouds. Containers (Docker) - virtualization of OS components.

The CU follows the project-based assessment model due to its eminently practical nature, and does not include a final exam.
A minimum attendance of 80% of classes, presentations, and other events that may be considered necessary for learning is required. Attendance and participation in theoretical-practical-laboratory courses are essential.
It is mandatory to complete 80% of the practical work.
The laboratory project, in groups, is mandatory. Groups of 5 or 6 students.
Summative assessment weights:
SA1: 2 practical assignments (12.25% each): 25% -> 2 group practical assignments (Hardware + OS, OS + Virtualization).
SA2: 2 mini-tests: 25% -> multiple-choice tests, carried out individually, on Moodle in the classroom, without consultation. Each mini-test covers half the subjects.
SA3: Laboratory project, with group presentation and demonstration and individual oral discussion: 50% -> the project work is eliminatory. Anyone who does not achieve a minimum mark of 9.5 out of 20 for the project fails the course.
Those who pass the project but fail the other components (< 9.5 out of 20) can make up the grade in the 2nd season by taking a test that includes the whole subject and is worth 50% of the grade, replacing the individual assignments and mini-tests (which also cover the whole subject). In order to take the 2nd season test, it is compulsory to pass the laboratory project, which contributes 50% to the final grade. For those who have already passed the 1st season assessment, they can take the 2nd season test to improve 50% of their final grade.
Formative assessment:
- Exercises and standard tests are made available on Moodle so that students can self-assess their knowledge.
- For those who require it, question timeframes are available for discussion and guidance on how to carry out the project.

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

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

Semester-long Assessment Mode:

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

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

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

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

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

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

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

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

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

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

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

Upon completing this course, students should:
OA1: Recognise AI fundamentals, linking predicate logic and knowledge-based systems.
OA2: Build and interpret simple machine learning models, distinguishing classification from regression, and defining suitable evaluation metrics.
OA3: Apply deductive and inductive reasoning, understanding how knowledge representation and heuristics guide decision-making.
OA4: Critically analyse the suitability of AI approaches in specific contexts, considering data, performance, and ethical implications.
OA5: Implement basic Generative AI strategies, focusing on prompt engineering and innovative content generation.
OA6: Demonstrate autonomous research and experimentation skills, integrating AI concepts into collaborative projects.
OA7: Reflect on AI constraints and socio-economic impacts, adopting a responsible stance in methodology selection and application.

S1: Essential AI concepts: definition, historical evolution, and multidisciplinary relevance.
S2: Predicate logic and knowledge-based systems: representation, deduction, and applications.
S3: Reasoning methods and heuristics: informed and uninformed search, selecting suitable strategies.
S4: Introduction to machine learning: basic notions, classification and regression algorithms, evaluation metrics.
S5: Simple predictive models: design, implementation, and results analysis in real-world scenarios.
S6: Generative AI: foundations, prompt engineering, and creative applications.
S7: Critical approach to AI limitations, ethical challenges, and socio-economic implications.

The assessment of this curricular unit follows the model of Assessment Throughout the Semester (ALS), provided for in the RGACC, and includes several moments, organized into three assessment blocks (AB), aimed at measuring progress and consolidating knowledge. It is made up as follows:
AB1: Written Exam (40%)
Assesses the fundamental concepts of artificial intelligence, machine learning, and generative AI. The exam evaluates both theoretical understanding and the ability to apply the concepts in practice.

AB2: Final Project in AI (60%)
Conducted individually or in groups, this project involves the design, implementation, and evaluation of a prototype or case study based on the course content. It encourages integration of knowledge and critical reflection on the topics covered. Evaluation is divided into a written report (60%) and an oral presentation (40%), delivered during the final session.

Additional rules:
Each assessment block requires a minimum score of 8.5 points.
If necessary, an individual oral discussion may be conducted to confirm knowledge.
The final course grade is the weighted sum of both blocks, with a minimum of 10 points required to pass.

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

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

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

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

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

Assessment throughout the semester includes:

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

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

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

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

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

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%.

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%).

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

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

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

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

LO1 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.

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%

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

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

Assessment throughout the semester:

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

Assessment by exam:
- One exam, weighted 100%

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

Upon completing this course, students should:
OA1: Recognise AI fundamentals, linking predicate logic and knowledge-based systems.
OA2: Build and interpret simple machine learning models, distinguishing classification from regression, and defining suitable evaluation metrics.
OA3: Apply deductive and inductive reasoning, understanding how knowledge representation and heuristics guide decision-making.
OA4: Critically analyse the suitability of AI approaches in specific contexts, considering data, performance, and ethical implications.
OA5: Implement basic Generative AI strategies, focusing on prompt engineering and innovative content generation.
OA6: Demonstrate autonomous research and experimentation skills, integrating AI concepts into collaborative projects.
OA7: Reflect on AI constraints and socio-economic impacts, adopting a responsible stance in methodology selection and application.

S1: Essential AI concepts: definition, historical evolution, and multidisciplinary relevance.
S2: Predicate logic and knowledge-based systems: representation, deduction, and applications.
S3: Reasoning methods and heuristics: informed and uninformed search, selecting suitable strategies.
S4: Introduction to machine learning: basic notions, classification and regression algorithms, evaluation metrics.
S5: Simple predictive models: design, implementation, and results analysis in real-world scenarios.
S6: Generative AI: foundations, prompt engineering, and creative applications.
S7: Critical approach to AI limitations, ethical challenges, and socio-economic implications.

The assessment of this curricular unit follows the model of Assessment Throughout the Semester (ALS), provided for in the RGACC, and includes several moments, organized into three assessment blocks (AB), aimed at measuring progress and consolidating knowledge. It is made up as follows:
AB1: Written Exam (40%)
Assesses the fundamental concepts of artificial intelligence, machine learning, and generative AI. The exam evaluates both theoretical understanding and the ability to apply the concepts in practice.

AB2: Final Project in AI (60%)
Conducted individually or in groups, this project involves the design, implementation, and evaluation of a prototype or case study based on the course content. It encourages integration of knowledge and critical reflection on the topics covered. Evaluation is divided into a written report (60%) and an oral presentation (40%), delivered during the final session.

Additional rules:
Each assessment block requires a minimum score of 8.5 points.
If necessary, an individual oral discussion may be conducted to confirm knowledge.
The final course grade is the weighted sum of both blocks, with a minimum of 10 points required to pass.

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

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

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

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%).

Upon completion of this course, students will be able to:
LO1. Understand the basic principles of how a computer network operates
LO2. Understand and comprehend the OSI and TCP/IP reference models
LO3. Understand how the main protocols used in everyday life work
LO4. Understand and comprehend the operation of transport-level protocols
LO5. Understand how devices are interconnected in a wired network
LO6. Be able to design, configure, and manage a computer network

PC1. Introduction to the physical layer. Presentation of the OSI and TCP/IP reference models
PC2. Introduction to the data link layer. Installation and configuration of a switch
PC3. Introduction to the network layer: IPv4 and IPv6 addressing; IPv4 protocol and subnet creation. Operation and configuration of a router
PC4. Exploration of TCP/UDP transport protocols. Congestion control
PC5. Exploration of the application layer: DNS, Email, and File Transfer
PC6. Traffic control between networks

It can be accomplished in one of the following ways:

1. Assessment throughout the semester:

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
-1 practical group assignment and its presentation (40%).

Note: Both tests have a minimum mark of 8 values, and it should be noted that the practical component is mandatory for the purposes of approval. The minimum grade 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.

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

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

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

The assessment process includes the following elements:

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

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

LO1: Integrate and apply knowledge, skills, and competencies acquired during the undergraduate course to real-world technology-based projects.
LO2: Apply user-centred co-creation methodologies in developing and prototyping innovative products or services.
LO3: Apply agile project management techniques to manage iterative prototyping cycles, ensuring timely delivery of technological solutions.
LO4: Know how to iteratively develop a functional prototype that addresses a problem specified in the Applied Project I module.
LO5: Critically evaluate the effectiveness and usability of developed prototypes through user testing.
LO6: Produce documentation for the innovative solution design, including architecture, hardware and software configuration, installation manuals, operation, and user manuals.
LO7: Deliver written and oral presentations and prepare a peer-review conference submission, demonstrating creativity, innovation, and self-directed learning.

S1 Solution space: User-centred co-creation methodologies applied to ideating the best technological solution for the project
S2 Agile management of the solution phase and project delivery
S3 Tools and techniques for iterative prototyping of the solution
S4 User studies and metrics for evaluating usability and task satisfaction
S5 Data analysis of evaluation results and solution iteration
S6 Solution documentation: backlog of requirements and user stories, design and architecture specifications, hardware and software configuration, installation, operation, and user manuals
S7 Oral, written, and audiovisual communication of results and conference submission with peer review. Elevator pitch for relevant stakeholders
S8 Demonstration in a workshop with relevant stakeholders in the software and applications field

Course with continuous assessment throughout the semester, without a final exam. Weights:
4 deliverables:
• D1 Solution Ideation Report, including Storyboard, User Journey, User Requirements, Technical Specifications, and its audiovisual presentation: 20%
• D2 Minimum Viable Product – MVP (on GitHub) and User Evaluation: 30%
• D3 Solution Design Report: Architecture (UML diagrams), Hardware and Software Configuration, Installation Manual, Operation Manual, User Manual: 20%
• D4 Presentation, MVP demo in workshop, and project discussion: 30%

Students who achieve a final weighted grade of 9.5 or higher will pass.

LO1: Apply user-centred co-creation methodologies in the development of innovative projects
LO2: Build empathy with the user and their organisation (define needs, obstacles, objectives, opportunities, current and desired tasks), define the problem and the issues addressed by the project
LO3: Conduct a systematic literature review and a competitive landscape analysis related to the identified problem and the raised issues
LO4: Identify the digital (including data collection), computational, and other resources needed to address the problem
LO5: Apply established knowledge of project planning, agile management, and project development to the problem solution
LO6: Develop and evaluate low or high-fidelity prototypes with experts
LO7: Participate in collaborative and co-creation dynamics and deliver written and oral presentations in the context of individual or group work

S1 User-centred co-creation methodologies based on Design Thinking and Design Sprint
S2 Presentation of case studies and project themes of digital technologies (product, service, or process)
S3 Selection of the project theme, individually or in groups, and its context within the organisation
S4 Problem space: building empathy with the user and their organisation, defining the problem considering business requirements, customer and user needs, and technological challenges
S5 Application of a systematic literature review methodology and its critical analysis. Competitor analysis (if applicable)
S6 Agile project management methodologies suitable for project work
S7 Identification of digital, computational, and other resources (including data collection) necessary for project development
S8 Low and high-fidelity prototyping
S9 Expert evaluation of the usability and task satisfaction of prototypes
S10 Communication of results

Course with continuous assessment throughout the semester, without a final exam. Weights of
5 deliverables:
• D1 Project Theme Definition, 5%
• D2 User Empathy, Problem Definition, and User Requirements, 20%
• D3 Literature Review and Project Development Planning, 20%
• D4 1st Prototype and Expert Evaluation, 25%
• D5 Presentation, Prototype Demo, and Project Discussion, 30%

Students who achieve a final weighted grade of 9.5 or higher will pass.

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%).

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.