LO1 Differentiate Agile and Waterfall. Technological trends.
LO2 Distinguish between Product Management and Project Management. Focus on product discovery and delivery.
LO3 Understand a business plan and the business model canvas (from a Lean Startup perspective).
LO4 Understand and frame the product vision within a business project. Understand how to meet user needs
LO5 Acquire skills in defining MVPs, user story mapping, and feature prioritization (Impact vs Effort Matrix and MoSCoW).
LO6 Understand the rituals and artifacts of Scrum and Kanban methodologies.
LO7 Understand the launch of a digital product: success criteria, planning, and connection with marketing.
LO8 Apply metrics (AARRR funnel) to evaluate the product. Interpret and analyze data. Evolve the product roadmap.
LO9 Collect user feedback in a continuous discovery process, adjusting the product to the market.

S1 Fundamentals and Agile Manifesto. Agile vs Waterfall. History. Technological trends.
S2 Product Management vs Project Management. Two-way development: product discovery and product delivery.
S3 Business ideas. Value capture. Business plan. Business model canvas. Lean Startup perspective.
S4 Product vision and user needs. Empathy map. Personas. User Stories (US). Requirements. Strategy. Competition. Product roadmap.
S5 MVP: Mapping of US. Definition of MVP for each use case: requirements and features.
S6 Agile methodologies: Kanban. Scrum rituals and artifacts: product backlog and sprint, epics, US, acceptance criteria.
S7 Feature prioritization.
S8 Agile planning: MVP delivery in weekly sprints.
S9 Product launch plan. Channel traction and marketing.
S10 Product efficacy and efficiency metrics. OKR. AARRR funnel. User interviews. Continuous discovery. Lessons learned. Market fit.

This course will adopt case-based learning as its central teaching and learning methodology, combined with project-based learning. The course will use peer tutoring and Design Thinking, a human-centered problem-solving methodology based on empathy, ideation, rapid prototyping, experimentation, and iteration as a motivation strategy and to develop creative and innovative capacities. This pedagogical approach aligns with the educational model of Iscte because the student is seen as an active agent in their learning process, knowledge is used as a tool for constructing and developing more knowledge, and applied in various contexts.

At the end of the course unit, the student should be able to:
LO1 Understand the nature of Digital Transformation (TD) processes and their impact on organizations, societies, and the world in general
LO2 Identify the main categories of Digital Technologies and relevant Business Models
LO3 Identify the main ingredients of a TD process and know how it should be managed
LO4 Discuss and understand concrete cases of TD in real organizations
LO5 Develop a concrete proposal for a TD process

The coherence between the learning objectives (OA) and the course program (S) is evident:
S1 - Introduction to TD, introduces the central theme, aligning with OA1 on understanding the nature and impact of Digital Transformation.
S2 - Technologies and Business Models, addresses digital technologies and relevant models and is in tune with OA2.
S3 - Ingredients of TD, addresses the key elements of TD and its management, aligning with OA3.
S4 - Exemplary Case Study, through the study of 1 case, aligns with OA4.
S5 - Specific Case Studies, reinforces OA4, offering a diversified view of TD in organizations.
S6 - Use Case Proposal, aligns with OA5 by inviting students to develop TD proposals for an organization of their choice.

Periodic Assessment:
Test 1 (30%)
Group Case Discussion (40%)
Individual Case Proposal (30%)
It is not possible to pass solely by taking an exam. The average of the 3 components must be 9.5 or higher

LO1 Understand the entrepreneurial process. Be aware of relevant technological trends and pertinent socio-economic dynamics.
LO2 Develop value propositions, business plans, and business models (within the Lean Startup context) that are triply sustainable in their social, environmental, and financial dimensions.
LO3 Be familiar with different applied innovation methodologies, and market and competition benchmarking techniques (Ansoff matrix).
LO4 Design a business model based on the Business Model Canvas methodology (BMC).
LO5 Promote the product and analyze the scalability of the business.
LO6 Prepare internationalization and marketing plans.
LO7 Seek out and analyze sources of financing.
LO8 Understand and know how to apply intellectual property regulations, patents, and supporting documentation.
LO9 Know how to communicate with peers and stakeholders in product development, through executive summaries and elevator pitch presentations

S1 Introduction to Entrepreneurship. Macro Context of Entrepreneurship.
S2 Methodologies: from business plan to Lean Startup. Recommended events. What is a business idea? How do we find business ideas?
S3 Examples of businesses and entrepreneurship initiatives that are triply sustainable.
S4 The innovation dilemma with case studies: innovation framework, innovation funnel, Open Innovation. Innovation in triply sustainable products.
S5 Market and competition benchmarking techniques (Ansoff matrix).
S6 Presentation of the business model canvas methodology (BMC).
S7 Product promotion and scalability analysis.
S8 Internationalization and marketing plans.
S9 Sources of financing.
S10 Intellectual property and patents.
S11 Presentation of a business idea. Public speaking (the art of pitching). Relevant stakeholders.

This course will adopt case-based learning as its central teaching and learning methodology, combined with project-based learning. As a motivation strategy and to develop creative and innovative capacities, the course will use peer tutoring and Design Thinking, a human-centered problem-solving methodology based on empathy, ideation, rapid prototyping, experimentation, and iteration. This pedagogical approach aligns with the educational model of Iscte because the student is seen as an active agent in their learning process, knowledge is used as a tool for constructing and developing more knowledge, and applied in various contexts.

LO1 Introduction to the Python programming language.
LO2 Understand the principles of data science and data mining and understand and be able to apply the Cross-Industry Standard Process for Data Mining (CRISP-DM) in practical cases, including understanding the business and data and preparing the data for modeling.
LO3 Can execute and debug Python applications and use the fundamental libraries in practical cases of data preparation, exploration, visualization, and analysis of data features.
LO4 Understand machine learning algorithms in supervised prediction problems (classification, regression, time series) and unsupervised clustering problems, and be able to apply and evaluate their performance in practical problems, using the Python language, in the context of the CRISP-DM methodology.
LO5 Understand and be able to apply ethical and privacy considerations in data analysis and discuss future trends in this domain.

S1 Introduction to the programming language (Python 3)
S2 Python development environments
S3 Control primitives, variables, expressions, and declarations. Objects and object classes. Functions, modules, and packages
S4 File operations. Interpretation of JSON, XML data. Database operations. Web scrapping
S5 Introduction to data science, the data cycle, and data mining. CRISP-DM model
S6 Data preparation and cleaning techniques
S7 Exploratory analysis and data visualization
S8 Selection and engineering of relevant data features
S9 Prediction methods in machine learning (classification, regression, time series). Essential algorithms, including their evaluation with performance metrics
S10 Clustering methods in machine learning. Essential algorithms
S11 Ethical considerations: privacy, security, and responsible data handling
S12 Emerging technologies and their impact on data analysis

Course unit with Periodic Assessment, not including a Final Exam. Weight of assessment:
Individual assignments, 80% mandatory (25%)
Laboratory project (group of 2), with individual oral discussion (50%)
2 multiple-choice mini-tests (25%)
If students fail in the regular period (grade < 10), they can take the 1st or 2nd term exam, which will count for 50% of the grade. It is mandatory to pass the group project or to carry out an individual project (50%).

LO1 Understand the principles of Human-Computer Interaction – HCI and User-Centered Design
LO2 Understand the fundamental perceptive and cognitive characteristics of human beings
LO3 Discover: create empathy with the user (needs, goals, tasks, problems) and gather requirements based on collected data
LO4 Design: understand and apply usability principles and "golden rules" in HCI design
LO5 Design: ideate solutions. Understand and apply techniques/rules of visual screen design and know how to create storyboards and low-fidelity prototypes (lo-fi)
LO6 Design: introduce emerging technologies and develop high-fidelity prototypes (hi-fi) with low-code/no-code technologies: Web (FIGMA, WordPress), Metaverse (Unity, EON Reality), mobility (FIGMA, Flutter), Generative AI (SuperAGI, Open.AI)
LO7 Decide and validate: Conduct heuristic evaluation, with experts, of the lo-fi and iterate to hi-fi. Design experimental studies of the hi-fi with end users. Produce an elevator pitch of the solution

S1 HCI: History, principles, state of the art, and applications. User-centered design. User experience, UX
S2. We, the humans
S3 User and task analysis. Empathy map. Personas. User scenarios and journeys. Prioritize user requirements
S4 Principles and golden rules of interface design. Usability
S5 Principles of screen visual design
S6 Sketching techniques and visual thinking. Storyboards
S7 Low-fidelity (lo-fi) paper solution prototypes
S8 Heuristic evaluation of lo-fi with experts and iterate to hi-fi (high-fidelity prototype)
S9 Design the high-fidelity prototype (hi.fi) based on interfaces/UX for emerging technologies: Web (FIGMA, WordPress), Metaverse (Unity, EON Reality), mobility (FIGMA, Flutter), Generative AI (SuperAGI, Open.AI)
S10 Hi-fi evaluation with users. Statistical analysis of evaluation data. Calculate usability metrics and user satisfaction and reiterate the design
S11 Elevator Pitch for investors and users.

Course unit with Periodic Assessment, not including a Final Exam. Weight of assessment:
Group project (group of 4), with individual oral discussion (70%)
2 multiple-choice mini-tests (30%)
If students fail in the regular period (grade < 10), they can repeat the mini-tests in the 1st or 2nd term, which will count for 30% of the grade. It is mandatory to pass the group project or to carry out an individual project (70%).