Graça Trindade

At the end of the Curricular Unit, the student should be able to:
LO1. Evaluate the data quality (missing cases and outliers), list descriptive data patterns (basic statistics, variable transformations, and relationships between pairs of attributes), detect properties of significant subpopulations of significant data, segment subjects according to several clustering techniques, and identifying patterns or trends in data, reproducing them when carrying out exercises.
LO2. Interpret results arising from data summarization techniques.
LO3. Differentiate subject segmentation models and choose the most appropriate one for a given problem.
LO4. Identify and apply data association techniques in the presence of large databases.

S1. Introduction to Exploratory data analysis
1.1. Types of data
1.2. Data quality: missing values and outliers
S2. Summarization techniques
2.1. Univariate and Bivariate Data Analysis
Descriptive measures and graphs.
2.2. Multivariate Data Analysis
2.2.1 Techniques to reduce the dimensionality of the input data: Principal Component Analysis (PCA), Principal Component Analysis for Categorical Data (CatPCA), and Multiple Correspondence Analysis (MCA).
2.2.2 Segmentation techniques: agglomerative hierarchical methods, K-Means, and Two-Step Clustering.
S3. Association Tecniques
(Market Basket Analysis)
Apriori, Carma, and Sequential algorithms
S4. Descriptive applications in real data, using the software IBM SPSS Statistics and IBM SPSS Modeler.

Assessment throughout semester or Assessment by exam.
Assessment throughout semester:
a) One Quiz (1h 15m) to be carried out online with a weight of 10%.
b) A group work with a weight of 40%.
c) One individual final test with a weight of (50%)
d) Weighted average of 8,5 for the individual assessments.
e) Minimum attendance of 2/3 of all the classes.
Assessment by Exam:
One written exam (60%) + an individual work (40%), requiring a min grade of 8,5 in each exam, provided that the final classification is at least 10 points.
In both modes, it may be necessary to carry out an oral discussion of group work or individual work.
Scale: 0-20 points

Nuno Santos

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

Ricardo Manuel Machado Pires

LO1. To know CRISP-DM methodology
LO2. To understand the framework to implement business analytics projects
LO3. To know the main computer tools for business analytics
LO4. To know same business analytics applications

P.1. Business analytics in the organizational context
P1.1. Framework to implement
P1.2. CRISP-DM methodology
P1.3. Analytics maturity
P2. Business analytics applications
P2.1. Data problems
P2.2. Business intelligence problems
P2.3. Analytics problems

1) Periodic evaluation:
a) Individual work with oral and digital presentation (70%)
(LO 1, 2, 3, 4)
b) Group work with possible discussion (30%)
(LO 3, 4)

Requires a minimum grade of 7.5 points in each element, attendance to classes of at least 4/5, and a minimum of 10 points in the final classification

Scale: 0-20 points

Given the practical nature of the content of the course, no exam is scheduled.

Renato Lopes da Costa

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 implementation and control in the strategic management process and its relationship with reporting and business analytics.
LG5. To create control systems for performance evaluation and information reporting for business management.
LG6. To develop critical thinking.
LG7. To have autonomy to plan learning processes and advance knowledge in the area under study.

T1. Strategy as starting point for business analytics.
T2. The strategic management process.
T3. Internal analysis of the organization based on its resources and dynamic capabilities.
T4. External environment analysis.
T5. Business strategies, new business models and digital transformation.
T6. Corporate strategies.
T7. Strategy implementation and control, reporting and business analytics.

Option 1 (Continuous Assessment)

i. Individual Written Test 50%
ii. Group Assignment 40%
iii. Seminar Report 10%

To successfully complete the course, students must obtain a weighted average of 10 points or more, with at least 8 in each component of the continuous assessment.

Option 2 (Final Exam)
To pass: 10 or more points.

20-point scale.

Luísa Domingues

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. Use efficient and effective languages and tools to extract information from structured and semi-structured data.

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. Semi-structured data

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.

Raul Laureano

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.

Ana Catarina Nunes

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

S1. Mathematical optimization models and techniques
1.1 Linear Optimization
1.2 Optimization with binary variables
1.3. Nonlinear Optimization
1.4 Applications of mathematical optimization in Management
1.5 Resolution using software
1.6 Make prescriptive recommendations
S2. Multiobjective optimization models and techniques
2.1 Modelos multiobjectivo
2.2 Applications of multiobjective optimization in Management
2.3 Resolution using software
2.4 Make prescriptive recommendations
S3. Heuristic optimization techniques
3.1 Introduction to heuristic optimization
3.2 Applications of heuristic optimization in Management
3.3 Resolution using software
3.4 Make prescriptive recommendations
S4. Other prescriptive models and techniques for Management

1. PERIODIC ASSESSMENT:
a) Written test (60%);
b) Group coursework with discussion (40%);
c) Attendance of at least 2/3 of the classes.
2. ASSESSMENT BY EXAM (1st and 2nd Season):
a) Written test (60%);
b) Individual project with discussion (40%);
Approval (in the Periodic or Exam evaluation):
i) Requires a minimum mark of 8.5 in each examination;
ii) An oral discussion may be required.
Scale: 0-20 points.

Patrícia Filipe

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
PC4. Stream Analysis and analytical models for Big Data
PC5. Business cases about Big Data problems and analytical solutions

1. Assessment throughout the semester: a) Group assignment with presentation (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.

Ricardo Manuel Machado Pires

Rute Saraiva

Learning outcomes:
LO1. Describe the key concepts and methodologies for developing a business case.
LO2. Describe the main concepts and frameworks for developing a data architecture.
LO3. Develop and communicate a business case supported by a data architecture.

At the end of the course, each student should be able to:
1) Analyze a business case;
2) Design a data architecture framework;
3) Identify decision criteria;
4) Select technological functionalities;
5) Draw up a value proposition; and
6) Define technological requirements for implementing the business case.

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 case, with collaborative work and brainstorming, through the recording of a video (pitch);
c) 30% with the individual delivery of the technological requirements of the business case;
d) 10% through individual oral discussion of the business case.

Requires a minimum grade of 10 points in all itens.

Scale: 0-20 points.

The course does not include evaluation by examination due to its seminar nature.

Raul Laureano

Filipe Ramos

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 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 (60%)
2) Critical review of a business analytics thesis (10%)
3) Writing, individually, or in a group, a scientific article and respective oral and digital presentation (10% theoretical sections + 10% empirical sections + 10% complete article submitted to a journal or scientific conference)

Requires a min. grade of 10 pts. in elements 1) and 2), 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%)