At the end of the curricular period, the student must:
LG1. Know and use the main concepts of univariate and bivariate descriptive statistics
LG2. Understand and apply the most important concepts of statistical inference within the scope of confidence intervals and parametric tests and non-parametric tests most appropriate to each situation.
LG3. Know how to apply a linear regression model
LG4. Use and know how to interpret SPSS Statistics outputs.

P1. Exploratory Data Analysis
P1.1 Univariate
P1.2 Bivariate
P2. Inferential analysis
P2.1 Parametric tests: t-test for a mean; t test for the difference of two means (independent samples); test for the equality of more than two means with independent samples.
P2.2 Non-parametric tests: Mann-Whitney test, Kruskal-Wallis test, and the Chi-Square test of independence
P3. Descriptive Multivariate Data Analysis
P3.1 Principal Component Analysis
P3.2 Clustering Analysis
P4 Predictive Multivariate Data Analysis
P4.1 Simple Linear Regression Analysis
P4.2 Multiple Linear Regression Analysis
P5 Utilize and interpret the IBM SPSS Statistics outputs

Periodic evaluation:
a) 2 Exercises with a weight of 15% each and a minimum grade of 7.5 points
b) Written test (70%) and a minimum grade of 7.5 points
The periodic evaluation requires attendance in at least 2/3 of the classes and approval will be obtained by students with a final minimum overall grade of 10, provided that they did not have a grade lower than 7.5 in any of the written individual tests.
Final evaluation:
Theoretical exam (70%) + practical exam (30%). It requires a minimum grade of 7.5 points in each exam, provided the final classification is at least 10 points (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.

To succeed in this course the student should be able to:
LO1. Describe 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.

Periodic evaluation:
- Individual test: 20%.
- Practical work (in groups): 80%
A minimum score of 10 in all components, Groups of 3 to 4 elements.
Alternatively, the student can be assessed by a final exam which is worth 100% of the grade.

For MSIAD students, the working groups should be the same as those of the Data Warehouse Systems I course.
The orals for the discussion of the practical work will take place via Zoom, on a date to be agreed with each group.
All the orals have to be concluded before the test date (frequency), in January (1st assessment period).

Students who do not fulfill the minimum mark in the group work will be assessed by examination in 1st season (counting 100% of the mark).
The 2nd season exam always constitutes 100% of the grade and may be taken
- by those who in the 1st season did not obtain a positive mark or were not assessed;
- to improve their grade (requires registration in the secretariat)

The successful taking of this course will enable students to be able to:
OA1. Understand the mission and objectives of a data warehousing system inside an organization and characterize in a clear way the process to implement them, justifying all the required investments.
OA2. Design a data warehousing system since its inception until its effective implementation and exploration.
OA3. Apply dimensional modeling techniques in the development of schemas for data warehouses.
OA4. Integrate analytical processing services in a data warehousing system environment

CP1. The area of Decision Support Systems
CP2. Introduction to Data Warehousing Systems
CP3. Infrastructures and services of a data warehousing system
CP4. Development lifecycle of a data warehousing system
CP5. Methodologies for the development of data warehousing systems
CP6. Planning of a Data Warehousing Project
CP7. Requirements Gathering and Analysis of Decision Agents
CP8. Conceptual, Logical and Physical Modeling of Data Warehouses
CP9. ETL Systems Design
CP10. Data warehousing systems administration
CP11. Data warehousing systems exploration
CP12. On-Line Analytical Processing (OLAP)

Periodic assessment:
- Individual test: 30%.
- Practical group work: 70%.
Alternative: Exam (100% grade and face-to-face)
Groups of 3 to 4 elements.
The minimum score in the individual test is 10. If in the practical work the student has a score of less than 10, but greater than or equal to 8, he or she will be submitted to an oral test for individual assessment. Anyone who does not meet the minimum score in periodic assessment (1st season) will have to take the exam in 2nd season.

To succeed in this course the student should be able to:
OA1. Know the different types and evolution of Decision Support Systems
OA2. Know the main principles of Data Science, and data-driven decision making and analytical thinking
OA3. Know the main characteristics of Data Warehouse and Business Intelligence (DW/BI) systems
OA4. Compare different Business Intelligence maturity assessment models
OA5. Know the critical success factors of developing DW/BI systems
OA6. Know the main techniques of Data Mining
OA7. Know the current challenges of Big Data Analytics
OA8. Know the main principles of data visualization (visual perception, visualization of structured and unstructured data)
OA9. Know how to solve cases using Python

CP1. Decision Support Systems: taxonomy, evolution, and fundamental characteristics
CP2. Data Science principles and data-driven decision making
CP3. Data Warehouse and Business Intelligence systems: fundamental characteristics, maturity assessment and critical success factors
CP4. Data Mining: fundamental techniques
CP5. Big Data Analytics challenges
CP6. Introduction to Data Visualization
CP7. Discussion of Data Science practical cases
CP8. Case Studies and Introduction to Python

As the course is eminently practical in nature, there is no examination mode of assessment.
Periodic assessment:
- Individual assignments in class (20%): one in Power BI (10%) and one in Python (10%).
- Written test (30%) taken in the last class of the course. Can be repeated in the 1st period
- Group work (50%): two homework assignments (25% each) carried out by groups of 3 to 4 students and subject to oral discussion.
Minimum mark of 10 in each component.
Special Season assessment consists of an individual assignment subject to oral discussion (70%) and a written test (30%).

To succeed in this course the student should be able to:
OA1. Apply data exploration analysis and data cleaning.
OA2. Apply linear, non-linear and logistic regression techniques.
OA3: Be acquainted with classification techniques Like Support Vector Machines and Neural Networks and Bayesian Classification.
OA4. Apply rule induction techniques to knowledge extraction problems
OA5. Apply cluster analysis techniques to knowledge extraction problems.
OA6. Understand how to obtain a good model.
OA7. Implement data mining models for real data.

CP1. Introduction to Data Mining: major corporate applications and major tools.
CP2. The data cycle process.
CP3. Descriptive Statistics for data exploration revision.
CP4. Linear, non-linear and logistic regression revisions.
CP5. Cluster analysis revision: major techniques and applications.
CP6. Tree based emsembles.
CP7. Bayesian inference techniques
CP8. Support Vector Machines.
CP9. Redes Neuronais.
CP10 Computer Vision introduction

The evaluation will be preferentially periodic, and can also be done through two assignments, each worth 30% of the final mark and a final exam of 40%.
Given the practical nature of the course, the assessment by examination implies the delivery of practical work which is worth 60% of the final mark, and the examination worth 40%.

LO1: Choose the lifecycle adequate to the characteristics of each BI project
LO2: Identify and apply good practices of Project Management. Recognize the maturity of the BI Project Management.
LO3: Plan, monitor, control and close a Business Intelligence Project Management, composed of ETL and Reporting components, going through the different stages of execution.
LO4: Use an agile methodology for the execution of BI Projects.
LO5: Illustrate the client relationship management and portfolio management with real BI projects.
LO6: Know the elements, scope, and stakeholders of a data governance program.
LO7: Identify and analyze the impact of the main critical success factors of BI projects.

CP1: Agile Project Management: SCRUM
CP2: The BI Project lifecycles and their characteristics
CP3: BI project management best practices and maturity models.
CP4: BI project management planning. The Analysis and Design phases of a project.
CP5: Monitoring and Control of a BI project. The Construction, Pre-Production, and Production phases of a BI project. Closing a BI project.
CP6: Agile Methodology for the execution of a BI project.
CP7: Concepts and case studies of customer relationship management. Concepts and case studies of portfolio management.
CP8: Data governance: elements, scope, and key stakeholders.
CP9: Critical success factors of BI projects.

Continuous assessment, with the following components:
- Participation in class/performance of exercises (10%)
- Group work, intermediate presentations, and final presentation (90%)
The students who fail the continuous assessment may take an exam in the appeal season (2nd season), counting 100% of the grade.
It is compulsory that students attend the classes involving practical exercises that are considered for assessment, the intermediate presentations, and the final presentation.

The successful taking of this course will enable students to be able to:
OA1. Understand the mission and objectives of an ETL system for a data warehouse (DW)
OA2. Characterize in a clear way an ETL system and its components of extraction, transformation and loading of data
OA3. Design an ETL system for a DW since its inception until its effective implementation and deployment.

CP1. Populating a data warehouse.
CP2. Techniques and strategies to populate a data warehouse.
CP3. Extract, transform and load (ETL).
CP4. ETL systems architectures.
CP5. Specification and design of an ETL system.
CP6. ETL systems development.
CP7. ETL systems monitoring.
CP8. ETL systems tuning.

Periodic assessment:
- Individual theoretical test: 30%.
- Practical group work: 70%.
Alternative: Exam (100% grade)
Groups of 3 to 4 elements.
The minimum score in the theoretical test is 10. If in the practical work the student has a score of less than 10, but greater than or equal to 8, he or she will be submitted to an oral test for individual assessment. Anyone who does not meet the minimum score in continuous assessment (1st season) will have to take the exam in 2nd season.

OA1: Perform tokenization, create dictionaries and perform other processing tasks to prepare text for classification tasks
OA2: Create numerical vector representations from text data
OA3: Build or apply classifiers
OA4: Perform "tagging" of text data
OA5: Cluster documents using topic models
OA6: Describe basic concepts and methods in text mining, for example document representation, information extraction, text classification and clustering, and topic modeling
OA7: Use available corpora, commercial and open-source text analysis and visualization tools to explore patterns
OA8: Understand conceptually the mechanism of advanced text mining algorithms for information extraction, text classification and clustering, and their applications in real-world problems
OA9: Choose appropriate technologies for specific text analysis tasks, and evaluate the benefit and challenges of the chosen technical solution

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 a periodic evaluation, not contemplating the mode of assessment by exam.

Evaluation components:
a) PROJECTS (2 assignments, 30% each), carried out during the school term;
b) TESTS (2 mini-tests: 5% each, final test: 30%), delivered and presented during the school term.

The grade of PROJECTS is limited to the grade of the TESTS + 6 values.

In case of failure, the TESTS component score can be replaced by a written test, performed during the period of the 1st, 2nd or special season (Artº 14 do RGACC).

Students may improve the grade of the TESTS component through a written test, to be held during the assessment period corresponding to the 1st season. Students who intend to do so must inform the professors as soon as the grades for the periodic assessment are released.

Attendance is not essential for passing.

To succeed in this course the student should be able to:?
LG1: Conduct a proper bibliographic research using the available resources;
LG2: Identify and formulate a research problem for a research project;
LG3: Develop a relevant and adequate literature review framing the research problem;
LG4: Substantiate the proposed solution using the methodologies and research instruments appropriate to the research problem;
LG5: Discuss critically his/her approach followed in the dissertation;
LG6: Communicate his/her work in writing and orally.

The nature of this course does not allow the definition of a specific program, given that it aims to help students in the ability to transfer previously held competencies to the development of a research project. Despite this, some of the contents that will be taught include:
PC1: Introduction to research
PC2: Defining the problem and the research goals (research proposal and Introduction chapter)?
PC3: Good practices for the development of the state of the art (literature review)?
PC4: Research methodologies: design science, case study, action research, questionnaires
PC5: Good practices for scientific writing and presentation of research projects?

The dissertation should be defended in public trials where the technical components, the form of the written work and the presentation and public defence will be evaluated. The Dissertation must be presented in accordance with the rules and deadlines established by ISCTE-IUL. The presence in the seminars is fundamental for the development of the project. The dissertation jury will be provided with information on the attendance of each student to the seminars of this UC