LG1. Describe univariate and multivariate data (measures and graphs).
LG2. Formulate hypotheses for a test; choose the most appropriate procedure; apply it, including validating any assumptions; interpret the results.
LG3. Apply a linear regression model.
LG4. Perform dimensionality reduction of data using Principal Component Analysis (PCA).
LG5. Group observations using basic cluster analysis techniques.
LG6. Use the statistical software R in the RStudio environment for all performed analyses.

P1. Exploratory data analysis for univariate and bivariate data: appropriate measures and graphical representations according to data type.
P2. Inferential analysis: basic principles, hypothesis formulation, errors, and p-value. Testing one, two, and several means (t-test and one-way ANOVA). Chi-square tests for goodness-of-fit and independence.
P3. Predictive analysis of multivariate data: Simple and Multiple Linear Regression.
P4. Descriptive analysis of multivariate data: Principal Component Analysis and Cluster Analysis (hierarchical methods and k-means).

Assessment throughout the semester:
2 Exercises with a weight of 15% each (minimum grade 7.5 out of 20)
Individual written test, 70% (minimum grade 7.5 out of 20)
Weighted average of the instruments, rounded to the units, must be at least 10 (out of 20) for approval.
Final evaluation:
Theoretical exam (70%) + practical exam (30%). Weighted average of the instruments, rounded to the units, must be at least 10 (out of 20).

To succeed in this course the student should be able to:
LO1. Know the fundamental principles of design of different types of Business Intelligence applications for data-driven decision support
LO2. Design standard reports for decision making
LO3. Design dashboards and scorecards for decision-making.
LO4. Define performance indicators with different aggregation levels
LO5. Know and apply the fundamental principles of data visualization for Business Intelligence and decision support systems
LO6. Compare and critically discuss different Business Intelligence interfaces.

CP1. Analytical cycle for data-based decision making
CP2. Types of Business Intelligence applications
CP3. BI application design and development methodologies
CP4. Design and development of standard reports
CP5. Designing performance indicators (including KPIs)
CP6. Design and development of dashboards and scorecards
CP7. Data visualization principles applied to the design of Business Intelligence interfaces

The student has two assessment methods: assessment throughout the semester and assessment by exam (for 100% of the grade). Given the practical nature of this UC, the assessment throughout the semester is recommended, which includes the development of a practical work.

Assessment throughout the semester consists of the following components:
- Practical work (in groups): 80%
- Individual test (frequência): 20%

Minimum mark of 10 in all components. The working groups have 3 to 4 members. There is no possibility of individual practical work. For MSIAD students, the working groups must be the same as those for the UC of Data Warehouse I.

The practical work must be handed in at the end of the term. The orals to discuss the practical work will be held via Zoom, on a date to be agreed with each group. All orals must be completed before the date of the test (frequência) to be held in the first assessment period.

Alternatively, the student can be assessed by a final exam worth 100% of the grade, in the 1st period, 2nd period and special assessment period.

Students who do not meet the minimum mark for the group work will be assessed by exam in the 1st assessment period (100% of the mark).

The 2nd assessment period exam always constitutes 100% of the grade and can be taken: (a) by those who did not obtain a positive grade in the 1st assessment period or were not assessed; (b) to improve their grade (requires registration at the secretariat).

To succeed in this course the student should be able to:
LO1. Apply data exploration analysis and data cleaning
LO2. Apply linear, non-linear and logistic regression techniques
LO3. Be acquainted with classification techniques Like Support Vector Machines and Neural Networks and Bayesian Classification
LO4. Apply rule induction techniques to knowledge extraction problems
LO5. Apply cluster analysis techniques to knowledge extraction problems
LO8. Understand how to obtain a good model
LO9. Implement data mining models for real data

CP1. Introduction to Data Mining, decision problems and applications
CP2. The data cycle process
CP3. Review of descriptive and exploratory statistics
CP4. Review of the main linear, non-linear and logistic regression techniques
CP5. Cluster analysis: main techniques and applications
CP6. Tree-based emsembles
CP7. Bayesian inference techniques
CP8. Support Vector Machines (SVM)
CP9. Neural Networks

The student has two assessment methods: assessment throughout the semester and assessment by exam (for 100% of the grade). Given the practical nature of this course, we recommend the assessment method throughout the semester, which includes the development of a practical assignment.

Assessment throughout the semester consists of the following components:
- Practical work (in groups, with an individual mark): 60%
- Peer review (in groups, with individual marks): 10%
- Individual test: 30%

Minimum mark of 10 in all components. The working groups have 2 to 3 members. There is no possibility of individual practical work.
The practical work must be handed in after the last lesson of the course in the academic term. This course is taught on an intensive basis, at the beginning of the semester, ending in the middle of the term.
Oral sessions to discuss the practical work will be held with two groups at the same time, where each group presents the peer review of the other work. The peer review report is handed in at the time of the oral. The individual test will be taken in the academic term, after the last lesson of the course.

Alternatively, the student can be assessed by a final exam worth 100% of the grade, in the 1st period, 2nd period and special assessment period.

Students who do not fulfil the minimum grade for the group work or the peer review will now be assessed by exam in the 1st period (counting 100% of the grade).

The 2nd period exam always constitutes 100% of the grade and can be taken: (a) by those who did not obtain a positive grade in the 1st period or were not assessed; (b) to improve their grade (requires registration at the secretariat).

To succeed in this course, students should be able to:
LO1. Know the fundamental characteristics and evolution of data-based Decision Support Systems
LO2. Distinguish between the design principles applied to operational systems and analytical systems
LO3. Know and apply dimensional modelling techniques for DW/BI
LO4. Know and apply the basic principles of agile dimensional modelling
LO5. Compare and criticise different dimensional models
LO6. Know the different development phases of a DW/BI system, according to R. Kimball's methodology
LO7. Know and apply the fundamental concepts of requirements gathering for a DW/BI project
LO8. Design a dimensional model for a specific business area with a view to developing a BI application
LO9. Express and explain the design decisions made at each stage of practical work

CP1. Introduction to data-based Decision Support Systems
CP2. Introduction to Data Warehouse and Business Intelligence (DW/BI) systems
CP3. Analytical Data Processing (OLAP) versus Transactional Data Processing (OLTP)
CP4. Methodology of R. Kimball for developing DW/BI systems
CP5. Dimensional modelling: Fundamental concepts
CP6. Agile dimensional modelling concepts
CP7. Dimensional modelling: Advanced concepts
CP8. Requirements gathering for dimensional modelling design
CP9. Planning and designing BI applications

The student has two assessment methods: assessment throughout the semester and assessment by exam (for 100% of the grade). Given the practical nature of this course, we recommend the assessment method throughout the semester, which includes the development of a practical assignment.
Assessment throughout the semester consists of the following components:
- Practical work (in groups, with an individual mark): 70% (binding partial submission to continue assessment throughout the semester, with qualitative assessment)
- Individual test: 30%

Minimum mark of 10 in all components.
The working groups have 3 to 4 members. There is no possibility of individual practical work. For MSIAD students, the groups must be the same as for the BI Application Design and Development course.
Alternative: assessment by final exam for 100% of the grade, in the 1st period, 2nd period and special assessment period.

The practical work has a binding partial submission to continue to be assessed throughout the semester, at the halfway point (typically in class 15). Each group will receive feedback and a qualitative assessment: A, B, C, D and F (now assessed by exam). All members of the group must be present at the presentation class for the partial delivery of the practical work. The deadline for handing in the practical work is the last week of classes, at the end of the term.

The orals to discuss the work will be held via Zoom, on a date to be agreed with each group. The marks for the orals (i.e. the mark for the practical work component) are individual. All orals must be completed before the date of the test, which takes place in the first assessment period.

Students who: (a) fail to hand in the first part of the assignment by the middle of the semester; or (b) fail to meet the minimum mark for the group assignment, will be assessed by exam in the 1st period (counting 100% of the mark).

The 2nd period exam always constitutes 100% of the grade and can be taken: (a) by those who did not obtain a positive grade or were not assessed in the 1st period; and (b) to improve their grade (registration required at the secretariat).

LO1: Choose the Life Cycle appropriate to the characteristics of each BI project
LO2: Identify and apply good Project Management practices. Recognise the maturity of BI Project Management.
LO3: Plan, monitor, control and close the Management of a Business Intelligence Project, comprising the ETL and Reporting components, going through the various execution phases.
LO4: Use an agile methodology to carry out BI projects.
LO5: Illustrate customer relationship management and portfolio management with real BI projects.
LO6: Identify and analyse the challenges of ethics and the General Data Protection Regulation in the context of BI projects.
LO7: Know the elements, scope and players in a data governance programme.
LO8: Compare different Business Intelligence maturity assessment models
LO9: Identify and analyse the impact of the main critical success factors for BI projects.

CP1: Agile Project Management: SCRUM
CP2: BI Project Life Cycles and their characteristics.
CP3: Best Practices in BI Project Management and BI Maturity Models.
CP4: BI Project Management Planning. The Analysis and Design phases of a BI Project.
CP5: Monitoring and Controlling a BI Project. The Construction, Pre-Production and Production phases of a BI Project. Closing a BI Project.
CP6: Agile methodology for executing a BI project.
CP7: Concepts and practical cases of Customer Relationship Management and Portfolio Management.
CP8: Ethics and the General Data Protection Regulation (GDPR)
CP9: Data governance: elements, scope and main players.
CP10: Critical success factors for BI projects.

The student has two assessment methods: assessment throughout the semester and assessment by exam (for 100% of the grade). Given the practical nature of this course, the assessment method throughout the semester is recommended, which includes the development of a project.

Assessment throughout the semester consists of the following components:
- Project (in groups, with an individual mark) (90%): includes intermediate deliveries and presentations and a final presentation.
- Participation in class/doing exercises in class: 10%.

Minimum mark of 10 in both assessment components throughout the semester. The working groups have 3 to 4 members. It is not possible to carry out individual projects.

Eligibility criteria for assessment throughout the semester: minimum class attendance of 60%. All students must attend classes involving practical exercises that are considered for assessment, intermediate presentations and the final presentation of the project.
The project has four binding intermediate deliveries to continue for assessment throughout the semester and a final presentation. Each group will receive informal and formative feedback on the intermediate deliverables to improve the quality of the project and the final presentation. Those who fail to complete the partial project deliverables will be assessed by exam.

The final presentation of the project takes place in the last week of classes.
Alternatively, the student can be assessed by a final exam worth 100% of the grade, in the 1st period, 2nd period and special assessment period.

To succeed in this course, students should be able to:
OA1. Understand the mission and objectives of a DW ETL system
LO2. Know and apply advanced dimensional modelling concepts
LO3. Validate dimensional modelling at a conceptual and logical level for a given business area
LO4. Know and describe the basic architecture of an ETL process
LO5. Plan the development of an ETL process and build a source-to-target mapping
LO6. Know and apply data quality and data profiling techniques
LO7. Build conceptual models of an ETL process
LO8. Implement an ETL process with the Microsoft SQL Server Integration Services (SSIS) tool
LO9. Express and explain the design decisions made at each stage of practical work
LO10. Know and distinguish the different DW/BI system architectures and their evolution over time.

CP1. Advanced dimensional modelling concepts: derived schemes and aggregates
CP2. Validating the conceptual and logical modelling of DW/BI systems
CP3. Basic architecture of an ETL process (data extraction, transformation and loading): ETL subsystems
CP4. Introduction to the Microsoft SQL Server Integration Services (SSIS) tool
CP5. Planning an ETL process; source-to-target mapping
CP6. Data quality and data profiling techniques
CP7. Conceptual modelling of an ETL process
CP8. Implementing an ETL process
CP9. DW architectures

The student has two assessment methods: assessment throughout the semester and assessment by exam (for 100% of the grade). Given the practical nature of this course, we recommend the assessment method throughout the semester, which includes the development of a practical assignment.
Assessment throughout the semester consists of the following components:
- Practical work (in groups, with an individual mark): 70%
- Individual test: 30%

Minimum mark of 10 in all components.
Working groups are made up of 3 to 4 members. There is no possibility of individual practical work. For MSIAD students, the groups should preferably be the same as those in Data Warehouse I, since the starting point for the practical work in this course is the case developed by the group in that course.

The deadline for handing in the practical work is during the assessment period, before the date of the 2nd period. The individual test takes place in the 1st assessment period.

The orals to discuss the work will be held via Zoom, on a date to be agreed with each group. The marks for the orals (i.e. the mark for the practical work component) are individual.

Alternative: assessment by final exam for 100% of the grade, in the 1st period, 2nd period and special assessment period.

The 2nd period exam always constitutes 100% of the grade and can be taken: (a) by those who did not obtain a positive grade in the 1st period or were not assessed; and (b) to improve their grade (registration required at the secretariat).

To succeed in this course, students should be able to:
LO1. Know the different types and evolution of Decision Support Systems
LO2. Know the fundamental principles of Data Science and data-based decision making and analytical thinking.
LO3. Know the fundamental characteristics of Data Warehouse and Business Intelligence (DW/BI) systems
LO4. Know the fundamental Data Mining techniques
LO5. Know the current challenges of Big Data Analytics
LO6: Identify the impact of ethical issues and data quality on data-based decision-making
LO7. Know the fundamental principles of data visualisation
LO8. Apply Python programming and data analysis techniques to practical cases
LO9: Develop critical thinking in exploring, analysing and communicating data for decision making
LO10: Construct and present a scientific poster

CP1. Decision Support Systems: taxonomy, evolution and fundamental characteristics
CP2. Principles of Data Science and data-based decision making
CP3. Data Warehouse and Business Intelligence systems: fundamental characteristics
CP4. Data Mining: fundamental techniques
CP5. Big Data Analytics challenges
CP6. Introduction to data visualisation
CP7: Ethics and data
CP8. Introduction to Python
CP9: Discussion and resolution of case studies in Integrated Decision Support Systems
CP10. Designing scientific posters

Given that the course is eminently practical and is an introduction to the master's programme in SIAD, there is no examination mode of assessment.
Assessment throughout the semester consists of the following components:
- Practical project (in groups, with an individual mark) – 50%: includes the submission of a scientific poster and a report.
- Presentation at the poster session (in groups, with an individual mark) - 20%.
- Peer review of a poster (in groups): 10%
- Class attendance and participation (individual grade): 20%

Minimum mark of 10 in all assessment components throughout the semester. Working groups have 2 members. It is not possible to carry out individual projects.

Eligibility criteria for assessment throughout the semester: class attendance of at least 60% (minimum of 14 out of 24 classes). All students must be present at the final presentation of the practical projects, held in a poster session.
The practical project is handed in after the end of classes. It should be noted that this course is taught on an intensive basis, starting at the beginning of the semester and ending in the middle of the term.
The final presentation of the projects at the poster session takes place after the last week of classes in the academic term.

The special assessment period consists of a written test (50%) and a practical assignment (50%).

OA1. Understand the fundamentals and challenges of Text Mining
OA2. Know techniques for preparing, cleaning, and representing documents
OA3. Apply Natural Language Processing methods
OA4. Classify texts using machine learning
OA5: Group documents using topic models
OA6. Apply Text Mining techniques in practice
OA7: Describe the main concepts, steps, and methods involved in developing Text Mining processes
OA8: Explain the functioning of advanced algorithms for information extraction and text classification and their application in handling real cases
OA9: Select appropriate techniques for specific text analysis tasks and evaluate the benefits and challenges of the adopted options

Introduction
CP1: Importance of large quantities of text, challenges and current methods
CP2: Unstructured vs. (semi-)structured information
CP3: Obtaining and filtering information, information extraction and Data Mining
Document Representation
CP4: Document pre-processing
CP5: Feature extraction: terms as features
CP6: Term weighting schemes
CP7: Vector space models
CP8: Similarity measures
Natural Language Processing
CP9: Language models
CP10: Morphology and part-of-speech tagging
CP11: Complex structures: syntactic analysis
CP12: Information extraction
Text Classification
CP13: Introduction to statistical machine learning
CP14: Evaluation
CP15: Generative classifiers
CP16: Discriminative classifiers
CP17: Unsupervised learning
CP18: Text Mining Resources
Case Study
CP19: Sentiment analysis
CP20: Topic classification and identification

This course uses assessment throughout the semester, not contemplating the mode of assessment by exam.

Assessment components:

a) TESTS (2 mini-tests: 5% each, final test: 30%), conducted during the teaching period;
b) ASSIGNMENTS (2 assignments, 30% each), submitted and presented during the teaching period.

Assignments can be done individually or in groups, with the number of group members defined in the assignment.

There are no minimum grades, but the ASSIGNMENTS grade is limited to the TESTS grade + 6 points. Examples of final grade calculation:

ASSIGNMENTS = 20, TESTS = 14 --> Final Grade = 18
ASSIGNMENTS = 20, TESTS = 10 --> Final Grade = 14 (the ASSIGNMENTS component grade was limited to 16 points = 10 + 6)

In case of failure, the TESTS grade can be replaced by a written exam to be taken during the evaluation period corresponding to the 1st round, 2nd round, or special round.

Students may improve their TESTS component grade through a written exam, to be taken during the evaluation period corresponding to the 1st round. Students who wish to do so must inform the teachers as soon as the periodic assessment grades are released.

Attendance is not a requirement for approval.

TTo be successful in this course, students should be able to:
LO1: Carry out a bibliographical search correctly using all the resources available for this purpose;
LO2: Identify and formulate a relevant problem for scientific research;
LO3: Prepare a literature review that is relevant and appropriate to the problem formulated;
LO4: Substantiate the proposed solution using the methodologies and research tools appropriate to the problem under analysis;
LO5: Argue critically for and against the approach taken in the dissertation;
LO6: Communicate in writing and orally the work done and the argument developed.

The nature of the course does not allow us to define a program with specific subjects, as it seeks to apply skills already acquired in order to achieve the goal of completing the master's thesis. Nevertheless, some of the subjects included in the course include:
CP1: Introduction to research
CP2: Formulating the research problem and objectives (dissertation proposal and introduction chapter)
CP3: Good practices for developing the state of the art (literature review)
CP4: Research methodologies: design science, case study and questionnaire research
CP5: Good practice in scientific writing and presentation of research work

The dissertation must be defended in public examinations where the technical component, the form of the written work and the public presentation and defense will be assessed. The dissertation or project must be presented in accordance with the rules and deadlines established by ISCTE. Attendance at seminar classes is essential for the development of the project. The dissertation/project jury will be provided with information on each student's attendance at the UC seminars.

Two seminars are held to monitor the dissertation or project, focusing on:
1. Dissertation Project and State of the Art (in the 1st semester);
2. Progress Report (in the 2nd semester).

Assessment throughout the semester (in the 1st semester) includes the following deliverables: dissertation proposal, introduction chapter, literature review chapter, planning of the next phases and a presentation on the work in progress.

Evaluation throughout the semester (in the 2nd semester) includes the following deliverables: progress report and a presentation on the work in progress.

Each semester, the deliverables must be handed in to the UC coordinating teacher before the monitoring seminars. Students receive formative feedback during the monitoring seminars and in each tutorial class.
Attendance and participation in the monitoring seminars is compulsory.
All deliverables of the assessment throughout the semester (1st and 2nd semester) must be submitted.