Students who successfully complete this course will be able to:
LO1 - Describe and contrast theories on leadership processes and organizational innovation
LO2 - Know and critically evaluate the psychosocial processes that condition leadership and innovation in organizations.
LO3 - Apply methods and techniques to diagnose and intervene in the main problems related to digital transformation and innovation in organizations.

PC1. Introductory approach to the concepts of digitization and digital platforms
PC2. The main drivers of digital disruption in organizations
PC3. Innovation and change in the dynamics of organizations
PC4. Team leadership and innovation in organizations.
PC5.Psychosocial processes in the leadership and implementation of a digital transformation strategy
PC6.Digital transformation in various sectors

Delivery of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.

LO1: Understand the principles and concepts of data analysis.
LO2: Apply data manipulation and cleaning techniques to prepare datasets for analysis.
LO3: Assess the challenges and opportunities of data analysis.
LO4: Explore successful case studies and best practices in data analysis.
LO5: Develop critical thinking skills to evaluate the impact of data analysis.
LO6: Apply Python libraries and tools for effective data manipulation, visualization and analysis.
LO7: Apply appropriate data visualization techniques to communicate knowledge.
LO8: Apply machine learning algorithms for data analysis and predictive analysis.
LO9: Evaluate the performance of machine learning models using domain-specific metrics.
LO10: Demonstrate ethical considerations in data analysis, privacy.

PC1. Introduction to data analysis: Key concepts and importance.
PC2. Technologies and Trends: IoT, Big Data, AI, ML, Cloud Computing.
PC3. Data manipulation and cleaning: Data pre-processing techniques.
PC4. Exploratory Data Analysis: Data analysis and visualization.
PC5. Feature selection and engineering: Methods for selecting relevant features.
PC6. Machine Learning Algorithms: Classification and regression models in data analysis.
PC7. Model evaluation and performance metrics: Evaluating the effectiveness of models.
PC8. Data visualization: Communicating knowledge effectively.
PC9. Ethical considerations: Privacy, security and responsible data processing in analytics.
PC10. Future trends: Emerging technologies and their impact on data analysis

Submission of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.

OA1 Know the concepts, models, state of the art and main applications of VR, AR and ER in solving current problems, through research and critical analysis of literature sources.
OA2 Understand the key technologies: VR (input/output channels); AR (data collection from the real environment and interpretation, 3D visualization and person-machine interaction)
OA3 Understand basic mathematical principles of 3D Computer Graphics systems and algorithms: representation of 3D geometric and hierarchical models (polygon meshes, scene graphs), their realistic and real-time visualization (3D transformation and visualization chain, visibility calculation, local and global lighting, texture mapping).
OA4 Know how to apply 3D modeling and VR, AR app development in Unity, UnReal, ARCore, ARKit, Hololens, EON Reality, Open XR environments.
OA5 Design laboratory experiments: 1RV + 1RA
OA6 Explore creativity, innovation, critical thinking, self-learning, peer review, teamwork, written and oral expression

PC1 Virtual Reality, VR: Definition. Model. Immersive and non-immersive VR. Historical evolution. Applications
PC2 Key technologies for VR: Output channel: projection, screens, stereoscopy, 3D audio and updating, new channels (smell, taste). Input channel: tracking, multimodal interfaces (speech, gesture, movement, touch), haptic and vestibular interfaces.
PC3 3D Computer Graphics: Definitions and concepts. 3D geometric modeling. 3D visualization. Virtual camera. Hierarchy of graphic objects. Scene graph. Light and color. Lighting and shading. Shadows. textures
PC4 CG distributed in VR. C5 VR laboratory
PC5 Augmented Reality, AR: Definition. Mixed Reality Model. Extended Reality, ER. Historical evolution. Applications
PC6 Key technologies for AR: Collection of data from the real environment and interpretation, visualization of virtual objects recorded in 3D, person-machine interaction (Tangible Interfaces and Multimodal Interaction)
PC7 RA Lab
PC8 Current and future trends in VR, AR and ER

Submission of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.

LO1: Understand the principles and concepts of IoT and Edge Computing systems.
LO2: Develop IoT and Edge Computing systems
LO3: Identify the main challenges and opportunities of IoT and Edge Computing systems.
LO4: Explore successful case studies and good practices of IoT and Edge Computing systems.
LO5: Assess the impact of IoT and Edge Computing systems in different sectors.

PC1 - Introduction to IoT systems and Edge Computing paradigms;
PC2 - Sensor layer and Arduino configuration;
PC3 - Communication layer with emphasis on LoRa;
PC4 - Cloud + IoT + Edge Infrastructures integration;
PC5 Introduction to the middle layer for Edge Cloud and Lightweight Container Middleware for Architectures;
PC6 - Data management and predictive analysis to support the development of applications at the Edge; PC7 - Applications in Health, Transportation and Smart Cities;
PC8 - Design applied to smart cities.

Submission of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam

Students who successfully complete this course will be able to:
LO1 - Analyze the main theories and good practices for implementing digital transformation
LO2 - Understand the context favorable to digital transformation
LO3 - Apply the main tools in real cases

PC1 - Main theories for digital transformation
PC2 - Main reference models for digital transformation
PC3 - Key case studies exemplifying the Digital Transformation

Delivery of 10 assignments, responding to the criteria of each teacher's request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.

Students who successfully complete this course will be able to:
LO1 Know how to establish a common vision and common sense in organisations
LO2 Know how to boost teams and culture
LO3 Know how to accelerate delivery and learning capacity
LO4 Know how to increase productivity and engagement

PC1:Old paradigm: organizations like machines
PC2: New paradigm: organizations as mutant living organisms
PC3: Strategic direction embedded throughout the organization
PC4: Networking to empower teams
PC5: Fast decision-making and learning cycles
PC6: Dynamic teams boost culture
PC7: Technology-based productivity

Entrega de 10 tarefas, respondendo aos critérios de cada solicitação do docente, com o peso de 50% da nota final e classificação mínima de 8,5 valores. As tarefas incluem atividades, desenvolvidas individualmente ou em grupo, nas sessões síncronas e em sessões assíncronas agendadas pelo docente, bem como intervenções em fóruns de discussão mediados.
- Projeto de aplicação previamente estabelecido pelo docente e respetiva discussão oral em grupo, com o peso de 50% na nota final e classificação mínima de 8,5 valores.
A média final terá de ser igual ou superior a 9,5 valores.
De acordo com o Regulamento Geral de Avaliação de Conhecimentos e Competências do Iscte, esta UC é classificada como UC de projeto, pelo que não contempla avaliação por exame.

LO1: Understand the principles and concepts of the application of IoT systems in the areas of health, industry, cities, transport, energy and agriculture designated as Smart.
LO2: Analyze the implications of the application of IoT systems in the areas of health, industry, cities, transport, energy and agriculture for organizations and society.
LO3: Identify the main challenges and opportunities in the application of IoT systems in the areas of health, industry, cities, transport, energy and agriculture.
LO4: Explore successful case studies and best practices in the application of IoT systems in the areas of health, industry, cities, transport, energy and agriculture.
LO5: Develop critical thinking and analysis skills to assess the impact of the application of IoT systems in the areas of health, industry, cities, transport, energy and agriculture in different sectors.

PC1. Introduction to IoT - Definition and key concepts
PC2. Smart Energy - IoT applications in energy, case studies
PC3. Smart Cities - IoT applications in smart cities, case studies
PC4. Smart Industry - IoT applications in industry, case studies
PC5. Smart Agriculture - IoT applications in agriculture, case studies
PC6. Smart Health - IoT applications in health, case studies
PC7. Smart Mobility - IoT applications in transportation, Case studies
PC8. Applied Work

Submission of 10 assignments, responding to the criteria of each teacher's request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.

OA1: Theoretical Understanding: Explain the theoretical foundations of deep learning, including RN, backpropagation and optimization techniques.
Describe popular RN architectures, such as CNNs, RNNs, LSTMs and GANs.
LO2: Practical Application: Implement and train deep learning models using frameworks such as TensorFlow, Keras, OpenCV and PyTorch.
Apply deep learning techniques to specific problems in computer vision (e.g. image recognition), natural language processing and generative AI
LO3: Analysis and Evaluation:
Evaluate the performance of deep learning models using appropriate metrics.
Identify and solve problems of overfitting, underfitting and other issues related to model training.
LO4: Design and Implementation:
Develop applied projects that use deep learning to solve practical problems. Integrate generative AI techniques into projects, such as generative adversarial networks (GANs) and transformers.

PC1 Image representation and operations
PC2 Image feature extraction
PC3 Introduction to machine learning
PC4 Classical neural networks
PC5 Convolutional neural networks
PC6 Knowledge transfer
PC7 Recurrent Neural Networks (RNNs) and LSTMs for sequential processing.
PC8 Generative Adversarial Networks (GANs)
PC9 Network architectures for object detection and identification
PC10 Network architectures for automatic content generation
PC11 Applied Project

Submission of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam

Students who successfully complete this course will be able to:
LO1-Apply BPMN to model business processes
LO2 - Apply procedural analysis techniques to identify problems
LO3 -Apply redesign heuristics to optimize and promote innovation
LO4 - Learning to model processes
LO5 - Learning to analyze processes
LO6 - Learning to redesign processes

PC1 - BPMN language
PC2 - Process analysis techniques
PC3 - Redesign heuristics

Submission of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.

OA1: Understand the principles and concepts of blockchain.
OA2: Analyze the implications of blockchain for organizations and society.
OA3: Identify the main challenges and opportunities of blockchain.
OA4: Explore successful case studies and best practices of blockchain in the public sector and companies
OA5: Develop critical thinking and analysis skills to assess the impact of blockchain in different sectors.

PC1. Introduction to Blockchain: Understanding the Fundamentals, What is Blockchain, Benefits and Challenges of Blockchain Adoption
PC2. Comparing blockchains: main features and capabilities, Public vs. private blockchains, Comparing blockchain platforms, Security and consensus mechanisms, Interoperability and standards
PC3. Blockchain in the public sector: Improving government applications, Blockchain use cases in the public sector, Legal and regulatory considerations, Implementation challenges and considerations
CP4. Blockchain business applications: innovation and efficiency, Blockchain technology overview, Blockchain in business: Key benefits, Sector-specific use cases, Business blockchain platforms and consortia
CP5 (DAOs): Governance and Tokenomics on Blockchain, Decentralized Governance Models, Tokenomics: Encouraging participation, Legal and regulatory considerations.

Delivery of 10 assignments, responding to the criteria of each teacher request, with a weight of 50% of the final grade and a minimum mark of 8.5. The assignments include activities, developed individually or in groups, in synchronous sessions and in asynchronous sessions scheduled by the teacher, as well as interventions in mediated discussion forums.
- Application project previously established by the teacher and respective oral group discussion, with a weight of 50% in the final grade and a minimum classification of 8.5 points.
The final average must be 9.5 or higher.
According to ISCTE's General Regulations for the Assessment of Knowledge and Skills, this course is classified as a project course, so it is not assessed by exam.