1. Relational Databases revision and Advanced (aggregated) SQL Queries in Mysql;
2. Introduction to No SQL Databases of databases implementation in MongoDB;
3. Mapping between Relational Databases and Document Databases;
4. Data extracting using JSON;
5. Redundancy and Data Distribution to manage fault tolerance and large information volume;
6. Data migration between different storage systems;
7. Introduction to data warehouse technology;
8. Data processing and integration to populate a Data Warehouse;
9. Information Extraction from a Data Warehouse (Querying and Reporting).

Assessment throughout the semester is done through a written test (minimum grade 7.5), 60% of the grade and a group project, 40% of the grade. Alternatively, there is assessment by exam.

Mandatory

2019,Andreas Meier , Michael Kaufmann SQL & NoSQL Databases
Models, Languages, Consistency Options and Architectures for Big Data Management, Springer

MongoDb Homepage[Text Wrapping Break]Golfarelli, M., Rizzi, S., Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Osborne Media; 1st Edition, May 26, 2009.
Damas, L. SQL - Structured Query Language " FCA Editora de Informática, 2005 (II);

Date, C.J. "An introduction to Database Systems" Addison-Wesley Publishing Company, sexta edição, 1995 (I.2, I.3, I.4, II);

NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison, A B M Moniruzzaman,?Syed Akhter Hossain, 2013 (https://arxiv.org/abs/1307.0191)

Optional

-

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).

Mandatory

Bakker, J. D. (2022). Applied Multivariate Statistics in R. Washington, DC: University of 536 Washington. Free access from https://uw.pressbooks.pub/appliedmultivariatestatistics/
Thulin, M. (2024). Modern Statistics with R. Second edition. CRC Press. ISBN 9781032512440, free access from https://www.modernstatisticswithr.com/

Optional

Speegle, D., & Clair, B. (2021). Probability, Statistics, and Data: A Fresh Approach Using R (1st ed.). Chapman and Hall/CRC. Free access from https://mathstat.slu.edu/~speegled/_book/
Dias Curto, JJ (2021) Estatística com R, aprenda fazendo : mais de 100 exercícios resolvidos, aplicações com dados reais, 1700 linhas de código em R para resolver, exercícios e aplicações. 1ª ed. ISBN 978-989-33-2076-1

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).

Mandatory

- Slides das aulas teóricas e exercícios disponibilizados pelos docentes
- Adamson, C. (2010) Star Schema: the complete reference. McGraw-Hill, USA
- Kimball, R. & Ross, M. (2013) The Data Warehouse Toolkit - the definite guide to dimensional modeling (3rd ed.). John Wiley & Sons, USA.
- Corr, L. & Stagnitto, J. (2011) Agile Data Warehouse Design - Collaborative Dimensional Modeling, from Whiteboard to Star Schema. DecisionOne Press, UK.
- Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., and Becker, B. (2008) The Data Warehouse Lifecycle Toolkit - practical techniques for building data warehouse and business intelligence systems (2nd ed.) John Wiley & Sons, USA
- Bakhshi, S. (2023) Expert Data Modeling with Power BI: Enrich and optimize your data models to get the best out of Power BI for reporting and business needs (2nd ed.) Packt Publishing

Optional

- Beaulieu, A. (2020) Learning SQL: Generate, Manipulate, and Retrieve Data (3rd ed.) OReilly Media
- Sharda, R., Delen, D., and Turban, F. (2019) Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th Ed). Pearson Education, Inc, USA
- Kimball, R., Ross, M., Becker, B., Mundy, J. & Thornthwaite, W. (2015) The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection. (2nd ed.). Wiley, USA
- Moss, L., Atre, S. (2003). Business Intelligence Roadmap. The Complete Lifecycle for Decision-Support Applications. Addison-Wesley Information Technology Series. 2003.

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%
- Participation and realization of ativities in class (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.

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 eight weeks, starting at the beginning of the semester and ending before the ending 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%).

Mandatory

- Materiais diversos fornecidos pelos docentes da UC.
- Sharda, R., Delen, D., and Turban, F. (2019) Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th Ed). Pearson Education, Inc, USA
- Python Data Science Handbook: essential tools for working with Data (2023) (2nd ed.) OReilly Media
- Provost, F., and T. Fawcett (2013) Data Science for Business. OReilly Media
- Nussbaumer Knaflic, C. (2015) Storytelling with data: a data visualization guide for business professionals. Wiley, USA.
- Siegel, E. (2016) Predictive Analytics, 2nd ed. John Wiley & Sons, USA

Optional

--

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).

Mandatory

- Slides e exercícios disponibilizados pelos docentes.
- Kimball, R., Caserta, J. (2004). The Data Warehouse ETL Toolkit - Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, John Wiley & Sons
- R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, and B. Becker (2008) The Data Warehouse Lifecycle Toolkit - practical techniques for building data warehouse and business intelligence systems, 2nd ed. John Wiley & Sons, USA
- Inmon, W. & Puppini, F. (2020) The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design. Technics Publications.
- Adamson, C. (2010) Star Schema: the complete reference. McGraw-Hill, USA

Optional

- Beaulieu, A. (2020) Learning SQL: Generate, Manipulate, and Retrieve Data (3rd ed.) OReilly Media
- Kimball, R., Ross, M., Becker, B., Mundy, J. & Thornthwaite, W. (2015) The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection. (2nd ed.). Wiley
- Inmon, W.H. (1996). Building the Data Warehouse, John Wiley & Sons
- Moss, L., Atre, S. (2003). Business Intelligence Roadmap. The Complete Lifecycle for Decision-Support Applications. Addison-Wesley Information Technology Series. 2003.
- R. Kimball, M. Ross (2013) The Data Warehouse Toolkit - the definite guide to dimensional modeling, 3rd Edition. John Wiley & Sons, USA.

CP1. Analytical cycle for data-based decision-making
CP2. Types of Business Intelligence applications
CP3. Designing performance metrics (including KPIs)
CP4. Methodology and good practices for developing dashboards
CP5. Methodology and good practices for developing scorecards
CP6. Design and development of standard reports
CP7. Analytical data processing (OLAP)
CP8. Data exploration within data warehouse/business intelligence systems
CP9. 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 course, we recommend the assessment throughout the semester, which includes the development of a practical assignment.

Assessment throughout the semester consists of the following components:
- Practical work (in groups): 80%
- Individual test (attendance): 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 for the Data Warehouse Systems II course.

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 (frequency) to be held in the 1st assessment period.

Alternatively, the student can be assessed by a final exam worth 100% of the grade, in the 1st season, 2nd season and special season.
Students who do not meet the minimum mark for the group work will now be assessed by exam in the 1st season (100% of the mark).
The 2nd season exam always constitutes 100% of the grade and can be taken: (a) by those who did not obtain a positive grade in the 1st season or were not assessed; (b) to improve their grade (requires registration at the secretariat).

Mandatory

- Cardoso, E. (2023) Business Intelligence e a gestão de performance. Iscte.
- Setlur, V. and Cogley, B. (2022) Functional Aesthetics for Data Visualization. Wiley, USA.
- Tominski, C. and Schumann, H. (2019) Interactive Visual Data Analysis. CRC Press
- R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, and B. Becker (2008) The Data Warehouse Lifecycle Toolkit - practical techniques for building data warehouse and business intelligence systems, 2nd ed. John Wiley & Sons, USAPress, UK
- Wexler, S., Shaffer, J., and Cotgreave, A. (2017) The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley
- Microsoft Power BI Self-paced Learning
https://docs.microsoft.com/en-us/power-bi/guided-learning/
- Tableau self-paced Learning
https://www.tableau.com/learn

Optional

- Preece, J., Rogers, Y., and Sharp, H., (2007) - Interaction Design: Beyond HCI. Wiley - Nussbaumer Knaflic, C. (2015) Storytelling with data: a data visualization guide for business professionals. Wiley, USA. - Nussbaumer Knaflic, C. (2019) Storytelling with data: let’s practice! Wiley, USA. - Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. New Riders. - Evergreen, Stephanie (2016). Effective Data Visualization: The Right Chart for the Right Data. USA. SAGE Publications Ltd - R. Kaplan, D. Norton (2008) The Execution Premium: Linking Strategy to Operations for Competitive Advantage. Harvard Business School Press. Versão traduzida em Português: Prémio de Execução, Edição de Actual Editora, 2009 - Caldeira, J. (2010). Dashboards: Comunicar eficazmente a informação de gestão. Edições Almedina. - Few, S. (2006) Information Dashboard Design - the effective visual communication of data. O´Reilly.

CP1. Introduction to Information Visualization for data-based decision making.
CP2. Interactive visual data analysis: development process.
CP3. Datasets and variables.
CP4. Visualization tasks. Taxonomies. Types of graphics and their use.
CP5. Visual perception: principles and application in graphics.
CP6. Information design: principles and applications in an analytical context.
CP7. Storytelling
CP8. Visual Analytics. Data visualization challenges for big data analytics and text analytics.
CP9. Evaluating information visualization solutions
CP10. Information visualization applications in different domains. New trends.

Given that the course is eminently practical, there is no assessment by exam.
Assessment throughout the semester consists of the following components:
- Practical project (in groups, with an individual mark) - 60%
- Individual test (quiz format) - 15%.
- Presentation at the assessment workshop (in groups, with individual marks) - 15%.
- Peer evaluation (group) - 10%

Minimum mark of 10 in all assessment components throughout the semester. The working groups have 2 to 3 members. There is no possibility of individual projects.

All students must be present at the final presentation of the practical projects, held in a workshop during the first assessment period.
The practical project is handed in during the first assessment period. The individual test (quiz format) takes place during the school term. Peer assessment, carried out in groups, comprises the assessment of a practical project worked on by another group and the submission of an assessment report (according to the template provided) before the assessment workshop.

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

Mandatory

- Tominski, C. & Schumann, H. (2019) Interactive Visual Data Analysis. CRC Press, USA.
- Setlur, V. and Cogley, B. (2022) Functional Aesthetics for Data Visualization. Wiley, USA.
- Friendly, M. & Wainer, H. (2021) A History of Data Visualization & Graphical Communication. Harvard University Press, USA.
- Nussbaumer Knaflic, C. (2019) Storytelling with data: let’s practice! Wiley, USA.
- Cairo, A. (2023) The Art of Insight: How Great Visualization Designers Think. Wiley, USA.
- Cairo. A. (2019) How Charts Lie: Getting Smarter about Visual Information. WW Norton & Co., USA.
- Cairo, A. (2016) The Truthful Art: Data, Charts and Maps for Communication. New Riders
- Munzner, T. (2014) Visualization Analysis and Design. AK Peters Visualization Series, CRC Press, USA.
- Lupi, G. & Posavec, S. (2016) Dear Data. Princeton Architectural Press.
- Lupi, G. & Posavec, S. (2018) Observe, Collect, Draw!: A Visual Journal. Princeton Architectural Press.

Optional

- Nussbaumer Knaflic, C. (2015) Storytelling with data: a data visualization guide for business professionals. Wiley, USA. - Preece, J., Rogers, Y., and Sharp, H., (2007) - Interaction Design: Beyond HCI. Wiley - Organ, N. (2024) Data Visualization for People of All Ages. CRC Press, USA. - Grant, R. (2019) Data Visualization. CRC Press, USA. - Evergreen, Stephanie (2016). Effective Data Visualization: The Right Chart for the Right Data. USA. SAGE Publications Ltd

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).

Mandatory

- Materiais diversos fornecidos pelos docentes da UC.
- Pang-Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar (2018) Introduction to Data Mining (2nd ed.). Addison-Wesley.
- VanderPlas, J. (2023) Python Data Science Handbook: Essential Tools for Working with Data (2nd ed.) OReilly Media.
- Webb, A. & Copsey, K. (2011) Statistical Pattern Recognition (3rd ed.). Wiley.

Optional

- Géron, A. (2022) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (3rd ed.). OReilly Media.
- Cady, F. (2017) The Data Science Handbook (1st ed.). Wiley.
- Foster Provost, Tom Fawcett (2013) Data Science for Business. What you need to know about data mining and data-analytic thinking (1st ed.). OReilly.

The main syllabus to be covered are
CP1: Introduction to research
CP2: Formulation of the research problem and objectives (dissertation proposal and introduction chapter)
CP3: Introduction to bibliographical research techniques
CP4: Introduction to research methodologies appropriate to the area of SIAD
CP5: Presentation and discussion of business and research projects and case studies

Assessment throughout the semester comprises the following components:
- Individual presentation: 40%
- Written document (individual) with the dissertation proposal: 50%
- Attendance: 10%
The written document must be handed in during the first assessment period. The students' individual presentations are made in class during the term.
In the event of failure, the student may submit an individual thesis proposal project (on a different topic) in the second or special assessment period, which will count for 100 per cent of the grade.
Due to the nature of the course, assessment by examination is not possible

Mandatory

Oliveira, L. A. (2011) Dissertação e Tese em Ciência e Tecnologia segundo Bolonha: Guia de boas práticas. Lidel
Oliveira, L. A. (2018) Escrita Científica: da folha em branco ao texto final. Lidel
Oates, B. (2006) Researching Information Systems and Computing (1st ed.). Sage Publications
Patton, M. Q. (2014) Qualitative Research & Evaluation Methods: Integrating Theory and Practice (4th ed.). Sage Publications

Optional

Presentation Zen: Simple Ideas on Presentation Design and Delivery, Garr Reynolds, New Riders Press, 2008 slide:ology: The Art and Science of Creating Great Presentations, Nancy Duarte, O'Reilly Media, 2008

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.

Mandatory

- Materiais diversos fornecidos pelos docentes da UC.
- A Guide to the Project Management Body of Knowledge PMBOK Guide - 7th Edition (2021) Project Management Institute.
- Marchewka, J.T. (2012) Information Technology Project Management - 4th Edition. Wiley.
- Carroll, J. & Morris, D. (2012). Agile Project Management in easy steps – 2nd Edition. In Easy Steps Limited.
- Hughes, R. (2012) Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum - 1st Edition. Morgan Kaufmann.
- Corr, L. (2011) Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema. DecisionOne Press.
- DAMA International (2017) DAMA-DMBOK - Data Management Body of Knowledge (2nd Edition). Technics Publications.
- Plotkin, D. (2021) Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance (2nd Edition). Academic Press, Elsevier.

Optional

- Pressman, R. (2009) Software Engineering: A Practitioner's Approach- 7th Edition. McGraw-Hill Education. - Meredith, J. R., Mantel, S. J., & Jr. (2011). Project Management: A Managerial Approach. John Wiley & Sons. - Carroll, J. (2012). Effective Project Management in Easy Steps. In Easy Steps. - Ladley, J. (2020) Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program (2nd Edition). Academic Press, Elsevier. - Moss, L. & Atre, S. (2003) Business Intelligence Roadmap: the Complete Project Lifecycle for Decision-Support Applications. Addison-Wesley.

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.

Mandatory

Oates, B. (2006) Researching Information Systems and Computing (1st ed.). Sage Publications- Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 Statement. https://www.prisma-statement.org/prisma-2020 - Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302- Patton, M. Q. (2014) Qualitative Research & Evaluation Methods: Integrating Theory and Practice (4th ed.). Sage Publications- Yin, R. (2017) Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.

Optional

- Oliveira, L. A. (2011) Dissertação e Tese em Ciência e Tecnologia segundo Bolonha: Guia de boas práticas. Lidel
- Oliveira, L. A. (2018) Escrita Científica: da folha em branco ao texto final. Lidel

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.

Mandatory

Charu C. Aggarwal, Machine Learning for Text, 2018, null, https://link.springer.com/book/10.1007/978-3-319-73531-3
Gabe Ignatow, Rada F. Mihalcea, An Introduction to Text Mining: Research Design, Data Collection, and Analysis — 1st Edition, 2017, null, https://methods.sagepub.com/book/an-introduction-to-text-mining
Dan Jurafsky and James H. Martin, Speech and Language Processing (3rd ed. draft), 2020, null, https://web.stanford.edu/~jurafsky/slp3/

Optional

Atefeh Farzindar and Diana Inkpen, Natural Language Processing for Social Media, Second Edition. Synthesis Lectures on Human Language Technologies, 2018, null, https://link.springer.com/book/10.1007/978-3-031-02167-1
Jacob Eisenstein, Introduction to Natural Language Processing, 2019, null, https://mitpress.mit.edu/9780262042840/introduction-to-natural-language-processing/