Renato Lopes da Costa

Department of Marketing, Operations and General Management

LG1. To develop some of the concepts underlying the strategic management process and understand the ways different theoretical perspectives approach this process.
LG2. To understand and apply the concept of dynamic capabilities and its approach to strategy.
LG3. To introduce some of the concepts underlying the formulation and development of business and corporate strategies, including business environment evaluation and stakeholder analysis.
LG4. To understand the importance of the formulation, implementation and control of strategic management and its connection to business analysis.
LG5. To develop critical thinking.
LG6. To have autonomy to plan learning processes and advance knowledge in the area under study.

T1. The Strategy and the Strategic Management Process: New Trends
T2. Internal analysis of the organization based on its resources and dynamic capabilities.
T3. External environment analysis.
T4. Business strategies, new business models and digital transformation. T5. Corporate strategies.
T6. Formulation, Implementation and Control of Strategy and its Connection with Business Analysis (e.g. business processes)

The assessment strategies are designed to effectively evaluate the achievement of the learning objectives:
Option 1 (Continuous assessment): This option includes an individual written test (50%) and a group project (50%). The individual test assesses theoretical knowledge (aligned with TM1), testing the understanding of strategic management concepts (LO1 to LO4). The group project assesses practical skills (aligned with TM2), analyzing students’ ability to develop strategic plans and apply analytical techniques in real-world scenarios (LO5 and LO6).
Option 2 (Final exam assessment): This option evaluates students based on a final exam, which will cover theoretical knowledge (LO1 and LO6). Although this method predominantly assesses theoretical understanding, the exam questions are designed to also test critical thinking and the application of knowledge in strategic decision-making contexts. Grading scale: 0–20.

Raul Laureano

Department of Quantitative Methods

LO1. Describe and demonstrate the application of classification techniques: decision trees, propositional rules and neural networks
LO2. Describe and demonstrate the application of regression techniques: linear regression, decision trees and neural networks
LO3. Apply, on analytical platforms, classification and regression techniques to solve real business problems

P1. Classification techniques:
P1.1. Decision trees and propositional rules
P1.2 Neural networks: the backpropagation algorithm
P1.3 Other algorithms for classification problems
P2.1. Linear regression
P2.2. Decision trees
P2.3 Neural networks: the backpropagation algorithm
P2.4. Other algorithms for regression problems
P3. Applications of classification and regression with real data, using IBM SPSS Modeler and IBM SPSS Statistics; or other

1) Assessment throughout the semester:
a) Written test (50%) - LO 1, 2
b) A group project with digital presentation (50%) and possible discussion (LO 1, 2. 3)

Requires a minimum grade of 7.5 points in each element, attendance to classes of at least 2/3, and a minimum of 10 points in the final classification.

2) Exam (all periods): a one week project with discussion and digital presentation (50%) and written test (50%), requiring minimum 10 points in each assignment to get approval.

Scale: 0-20 points.

Luísa Cristina da Graça Pardal Domingues Miranda

Department of Information Science and Technology

At the end of this learning unit, the student must be able to (LG):
1. Understand the importance of database management in data science.
2. Analyse and design data models for operational and analytical systems.
3. Differentiate between relational, dimensional, and contemporary data architectures.
4. Propose justified and well-founded solutions to analytical problems, considering efficiency, quality, and sustainability.

A. Data management background in the data-science universe
B. Relational Schemas Design
1. Relations and primary keys
2. Foreign keys and rules of integrity;
3. Critical analysis and construction of a relational model;

C. Language S.Q.L
1. Simple Interrogations;
2. Aggregation and Grouping Functions;
3. Chained Interrogations;
4. Creation of Views

D. Optimization

E. Dimensional Model
1. Dimensional model design
2. Dimensional vs relational model
3. ETL & data quality

F. Data Architectures and Ecosystems: New Trends
1. Evolution of data architectures
2. Storage and organization
3. Data integration and quality
4. Governance and sustainability

Assessment by exam (1st Period, 2nd Period, and Special Period):
* Written test (100%)
- Approval: grade >= 10 points.

Assessment throughout the semester:
* Group work with discussion - phased delivery throughout the semester and discussion at the end of the semester (50%);
* Individual written test - 1st Period date (50%).
- Approval: Final classification >=10 points; and Individual written test >=8 points.
- Failure to attend the discussion implies canceling the group work as an assessment item.
- The final grades of group work will depend on each student's performance in the discussion and may vary between 0 (zero) and 20 points.

Ricardo Pires

Department of Quantitative Methods

Rute Saraiva

Department of Quantitative Methods

LO1: Describe the main concepts and frameworks for developing a data architecture.
LO2: Apply methodologies and tools appropriate to the project (e.g. analyze a business project, design data architecture framework, identify decision criteria, select technological functionalities, and define technological requirements for implementing).
LO3: Apply data analytics techniques (e.g. cleaning, preprocessing, and integrating real data) appropriate to the project.
LO4: Analyze data (e.g., summarize and visualize data effectively, modelling) and interpret results critically.
LO5: Communicate results clearly and in a structured manner (including draw up a value preposition).

P1. Fundamentals of data architecture
P2. Data storage and retrieval
P3. Data integration
P4. Data analysis, modelling and visualisation
P5. Data security and quality
P6. Architectures with emerging technologies
P7. Problem identification
P8. Justification of proposals
P9. Identifying risks and benefits
P10. Definition of technological requirements
P11. Communication business cases

Students’ evaluation throughout the semester is based on the following components: a) 10% in accordance with student's attendance at classes.
b) 50% with group work, where students must demonstrate their ability to develop a business project, with collaborative work and brainstorming, through the recording of a video (pitch) and a written report (project documentation). c) 40% through individual oral discussion of the business project.
Requires: i) a minimum grade of 10 points in all itens; ii) successful completion of three recommended e-learning courses (8 hours); and iii) participation throughout the year in the following activities: one computer tool workshop (8 hours), one one-day datathon/hackathon/student challenge, and two student-organized seminars.
Scale: 0-20 points.
The course does not include evaluation by examination due to its project-based learning nature.

Carlos José Lúcio Martins

Department of Quantitative Methods

At the end of this Curricular Unit, the student is expected to be able to:
LO1: Identify prescriptive and cognitive decision support models appropriate for applications in Management.
LO2: Identify prescriptive and cognitive decision support techniques appropriate for applications in Management. LO3: Develop prescriptive and cognitive models for applications in Management.
LO4: Use software to solve prescriptive and cognitive cases.
LO5: Interpret and produce recommendations based on the results obtained.

PC1. Prescriptive models and techniques
1.1 Concepts
1.2 Main optimization models (e.g., linear, integer, nonlinear, multiobjective)
1.3 Main optimization techniques (e.g., exact, heuristics)
1.4 Monte Carlo simulation
1.5 Aplications in Management
PC2. Cognitive analytics
2.1 Concepts
2.2 Applications in Management (e.g., AI use cases with different types of AI)
2.3 Showcase with software (e.g., WatsonX)
PC3. Solving prescriptive and cognitive cases with software (e.g., Excel, SPSS Modeler, SAP Signavio, WatsonX, LLMs)

Assessment throughout the semester or assessment by exam:
1. ASSESSMENT THROUGHOUT THE SEMESTER:
a) Written test: i) weight of 60%; ii) minimum classification of 8.5;
b) Group project: i) weight of 40%; ii) Groups of 4 students; iii) with presentation and oral discussion;c) Attendance of at least 2/3 of the classes taught;
d) Approval: minimum weighted average of 9.5.
2. ASSESSMENT BY EXAM (1st and 2nd Season):
a) Written test: i) weight of 60%; ii) minimum classification of 8.5;
b) Individual project: i) weight of 40%; ii) with presentation and oral discussion;
c) Approval: minimum weighted average of 9.5.
In both assessment methods, an oral discussion may be required.
Scale: 0-20 points.

Patrícia Andreia da Silva Filipe

Department of Quantitative Methods

LG1. Understand the basic concepts of Big Data and its implications in different fields of management LG2. Apply analytical models with Big Data
LG3. Evaluate alternative analytical solutions with Big Data

PC1. Big Data: Introduction, challenges, trends and applications
PC2. Big Data characterization: The V's of Big Data
PC3. Big data technologies: Hadoop Ecosystem. HDFS, Map-reduce and Spark. Use of Analytics Platform (e.g. KNIME)
PC4. Stream Analysis and Analytical Models for Big Data: Artificial Intelligence, Machine Learning and Deep Learning (e.g. FNN, Autoencoder, RNN, LSTM, CNN)
PC5. Business cases about Big Data problems and analytical solutions (e.g. Fraud Detection, Demand Prediction, Real-Time Analytics, ...)

Assessment throughout the semester:
1. Assessment throughout the semester: a) Group assignment with presentation (e.g. KNIME (or other ) student challenge) (50%). (LG 2, 3); b) Individual test (50%). (LG 1, 2, 3).
Approval by assessment throughout the semester requires:
- students attendance of at least 75% of classes;
- minimum grade of 7,5 in the individual test and the group assignment;- minimum final grade of 10.
2. Assessment by exam: Individual test (50%) and Individual assignment with presentation (50%).
Approval by exam requires:
- minimum grade of 10 for each of the evaluation elements; - minimum final classification of 10.
Scale: 0-20

Maria Gabriela Matias da Silva

Department of Quantitative Methods

Upon successful completion of the course unit, students should be able to:
LO1. Assess data quality using summarisation techniques;
LO2. Describe and apply descriptive and diagnostic analysis techniques (e.g., dimensionality reduction, clustering, association rules);
LO3. Interpret and communicate the results of descriptive and diagnostic analyses;
LO4. Apply descriptive and diagnostic techniques to solve real-world business problems, in analytical platforms.

S1. Introduction to descriptive and diagnostic analysis
1.1. Data types
1.2. Summarisation techniques for univariate and bivariate data
1.3. Assessing data quality: missing responses and outliers
S2. Descriptive and diagnostic techniques for multivariate data
2.1. Dimensionality reduction: Principal component analysis (PCA)
2.2. Clustering (e.g., hierarchical agglomerative method and K-means)
2.3. Association rules (market basket analysis; e.g., apriori and sequential algorithms)
S3. Practical applications of descriptive and diagnostic techniques with real data, using IBM SPSS Modeler and IBM SPSS Statistics (or other software)

Assessment throughout the semester
a) Individual written test (60%);
b) Group applied project (40%), with possible digital presentation or discussion.
Approval requirements:
a) Minimum mark of 7.5 out of 20 in each component;
b) Minimum final mark of 10 out of 20;
c) Attendance of at least two-thirds of the scheduled classes.

Assessment by exam (first, second and third sitting)
Individual applied project (40%), with possible digital presentation or discussion, and individual written exam (60%), both requiring a minimum mark of 7.5 out of 20. Minimum final mark: 10 out of 20.

The written test or exams are closed-book.

Raul Laureano

Department of Quantitative Methods

LO1. To know business analytics concepts and methodologies, focusing on the business problem (e.g. CRISP-DM).
LO2. To know and describe the main computer tools for business analytics.
LO3. To know and describe some real business analytics applications.

P.1. Business analytics in the organizational context
P1.1. Concepts
P1.2. Framework to implement
P1.3. CRISP-DM methodology
P1.4. Analytics maturity
P1.5. Other relevant topics (e.g. ethics, sustainability)
P2. Business analytics applications
P2.1. Data problems
P2.2. Business intelligence problems
P2.3. Business analytics problems
P3. Computer analytical tools
P3.1 Business Intelligence
P3.2 Data analytics and artificial intelligence
P3.3 Other tools

Assessment throughout the semester:
a) Successful completion of two e-learning courses (5% + 5%) - LO 1, 2
b) Individual work with oral and digital presentation (50%) - LO 1, 2, 3
c) Group work with digital presentation and possible discussion (40%) - LO 2, 3
Requires: i) a minimum grade of 7.5 points in b) and c) elements; ii) attendance to classes of at least 4/5; iii) a minimum of 10 points in the final classification; and iv) participation throughout the semester in the following activities (to be confirmed at the beginning of the semester): two computer tools workshops (8 hours each; e.g., Python, KNIME, SPSS Modeler, SAS, Qlik, Power BI, Tableau); one student-organized roundtable; two student-organized seminars; and two open days.
Scale: 0-20 points

Given the practical nature of the content of the course and the constant interaction with professionals, no exam is scheduled.

Nuno Santos

Department of Digital Technologies

At the end of the curricular period of this UC, the student must:
LG1. Identify and apply the concepts and technologies associated with the area of ??unstructured text and social network analysis with a view to implementing solutions that can assist decision-making in a managerial context.
LG2. Apply text mining techniques to better understand and manage business problems.
LG3. Develop study, personal research and communication skills in Text Mining. LG4. Know the context of NLP in current Artificial Intelligence.

PC1. Introduction to Text Mining
PC2. Tokenization, Dictionary Creation and Corpus Preparation
PC3. Clustering Methods for Text Mining
PC4. Classification Methods for Text Mining
PC5. Practical cases on using clustering methods in text mining for business
PC6. Application of Text Classification Cases Applied to Management: Sentiment Analysis. PC7. Advanced NLP Topics.

1) Assessment throughout the semester: a) Individual test (50%).
b) Group work 1 - Tools (Image, Video, Sound, or Text) (15%).
c) Group work 2 - Application/Project (Image, Video, Sound, or Text) (25%).d) Class participation (10%).
Approval: a) min. 7.5 points in each of the assessment elements; b) minimum final classification. 10 values.
2) Assessment by exam (1st season): written test (100%), with a minimum grade of 10.
3) Assessment by exam (2nd season): written test (100%) with a minimum grade of 10.
Scale: 0-20 points.

Raul Laureano

Department of Quantitative Methods

LO1. To know how to differentiate: a dissertation and a business analytics project
LO2. To know how to select a research problem and review the literature
LO3. To know how to write and present a business analytics thesis project and a scientific article

P.1 The stages of research
P1.1.Types of thesis
P1.2. Identifying the research problems
P1.3. Planning the research stages
P2. Doing a literature review and identifying the main literature sources
P3. Types and stages of empirical work in business analytics
P3.1. Identify methodologies
P3.2. Identify data, analytical tools and techniques to solve the research problem
P4. Development and formal presentation of the research project and scientific article
P4.1. Tutorial and individual or small group monitoring of the development and improvement of research work

Evaluation throughout the semesters:
1) Thesis project with oral presentation (60%)
2) Active participation in class (10%) - suggested classroom activities completed
3) Writing, individually, a scientific article and respective oral and digital presentation: 10% theoretical section; 10% empirical section; 10% complete article submitted and presented at scientific conference, with the supervisor agreement (e.g. as coauthor)

Requires a min. grade of 10 pts. in elements 1) and 2), article submission according to the conference’s requirements, attendance to classes of at least 2/3, and a min. of 10 pts. in the final classification.

Scale: 0-20 points.

This course does not have a final exam.

LO1. Writing a dissertation
LO2. Public oral presentation of the synthesis of the thesis

P1. Writing the introduction and abstract (resumo);
P1.1. Definition of a research problem and goals;
P2. Writing the literature review;
P3. Writing the methodology
P4. Writing the results and its discussion
P5. Writing conclusions
P5.1. Contributions/Implications in academic and practical terms
P5.2. Limitations and new research paths
P6. Communicate orally the synthesis

Assessment throughout semesters
1) Written presentation of the dissertation (80%)
2) Oral presentation with the synthesis of the dissertation followed by a public defense with a jury (20%)

Minimum classification: 10 points; scale: 0 - 20 points

LG1. Writing a master project
LG2. Writing a synthesis of the master project
LG3. Preparing a public oral presentation of the synthesis of the master project

P1. Writing the introduction and abstract (resumo);
P2. Definition of the business problem and diagnosis of organizational environment;
P3. Definition of the project goals;
P4. Applied literature review;
P5. Defining the analytical objectives and monitoring metrics;
P6. Data understanding and preparation;
P7. Data analysis methods (modelling) and evaluation;
P8. Writing conclusions and defining new projects paths;
P9. Evaluation of impacts and possibilities of control of results.

- Written presentation of the thesis (80%)
- Oral presentation with the synthesis of the project followed by a public defense with a jury (20%)