Accreditations
The MSc in Integrated Business Intelligence Systems is two years long and confers 120 ECTS credits, divided as follows: 60 credits in compulsory curricular units, 12 credits in optional curricular units and 48 credits in the dissertation.
Optional curricular units should be chosen (in connection with the course coordenation) in alignment with the specialization profile of the student. Such electives might develop the student's skills in areas like Big Data, Machine Learning and Multi-criteria Decision Analysis.
Typically, the most-chosen electives are:
- Database Fundamentals (MSIAD)
- Advanced Topics in Business Intelligence
- Machine Learning
- Big Data Algorithms
- Computational Language Processing
- Computational Modeling of Complex Social Systems
The MSc also requires the completion of a dissertation in Integrated Business Intelligence Systems that corresponds to 48 ECTS credits. In the new curricular plan, the course "Dissertation in Integrated Business Intelligence Systems" has a lecture component of 36 hours, divided among the two semesters of the second year of the course. These lecture hours include two mandatory seminars for monitoring the process of dissertation work.
Programme Structure for 2024/2025
| Curricular Courses | Credits | |
|---|---|---|
| 1st Year | ||
|
Data Analysis for Business Intelligence
6.0 ECTS
|
||
|
Design and Development of Business Intelligence Applications
6.0 ECTS
|
||
|
Data Warehouse Systems I
6.0 ECTS
|
||
|
Data-Driven Decision Making
6.0 ECTS
|
||
|
Patterns and Knowledge Extraction Guided by Data
6.0 ECTS
|
||
|
Business Intelligence Systems Project Management
6.0 ECTS
|
||
|
Data Warehouse Systems II
6.0 ECTS
|
||
| 2nd Year | ||
|
Text Mining
6.0 ECTS
|
||
|
Dissertation in Integrated Decision Support Systems
42.0 ECTS
|
Data Analysis for Business Intelligence
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).
Title: 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/
Authors:
Reference: null
Year:
Title: 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
Authors:
Reference: null
Year:
Design and Development of Business Intelligence Applications
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).
Title: - Cardoso, E. (2023) Business Intelligence e a gestão de performance. Iscte.
- 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
- 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.
- Wexler, S., Shaffer, J., and Cotgreave, A. (2017) The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley
- 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
- Microsoft Power BI Self-paced Learning
https://docs.microsoft.com/en-us/power-bi/guided-learning/
Authors:
Reference: null
Year:
Title: - 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
- N.G., Olve, J. Roy, M. Wetter (2004) Performance Drivers: a practical guide to using the Balanced Scorecard, John Wiley & Sons, Ltd (original Swedish Edition published in 1997)
- R. Kaplan, D. Norton (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, Harvard Business School Press
- Few, S. (2006) Information Dashboard Design - the effective visual communication of data. O´Reilly.
- Eckerson, W. (2006). Performance Dashboards: Measuring, monitoring and managing your business. John Wiley & Sons.
- Caldeira, J. (2010). Dashboards: Comunicar eficazmente a informação de gestão. Edições Almedina.
- Ware, Colin (2004) Information Visualization: Perception for Design, 2nd Ed, Morgan Kauffman
Authors:
Reference: null
Year:
Data Warehouse Systems I
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).
Title: - 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
Authors:
Reference: null
Year:
Title: - 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.
Authors:
Reference: null
Year:
Data-Driven Decision Making
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%).
Title: - 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
Authors:
Reference: null
Year:
Title: - 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
- Tan, P-N., Steinbach, M., and Kumar, V. (2006) Introduction to Data Mining. Addison-Wesley.
- Cairo, A. (2016) The Truthful Art: Data, Charts and Maps for Communication. New Riders
Authors:
Reference: null
Year:
Patterns and Knowledge Extraction Guided by Data
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).
Title: Andrw R. Webb, Keith D. Copsey. Statistical Pattern Recognition, 3rd Ed., Wiley, 2011.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer, 2013.
Cathy O'Neil, Rachel Schutt. Doing Data Science: Straight Talk from the Frontline. O'Reilly Media, 2013
Authors:
Reference: null
Year:
Title: Foster Provost, Tom Fawcett (2013) Data Science for Business. What you need to know about data mining and data-analytic thinking, 1st edition. O?Reilly, 2013
Authors:
Reference: null
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Business Intelligence Systems Project Management
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.
Title: Plotkin, D. (2021) Data Stewardship - An Actionable Guide to Effective Data Management and Data Governance (2nd Edition). Academic Press, Elsevier.
Ladley, J. (2020) Data Governance - How to Design, Deploy, and Sustain an Effective Data Governance Program (2nd Edition). Academic Press, Elsevier.
Lawrence Corr, Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema, ISBN 9780956817204
Ralph Hughes, Agile Data Warehousing Project Management, ISBN 9780123964632
Roger S Pressman, Software Engineering: A Practitioner's Approach, 7th Ed, ISBN 0073375977
Carroll, J. (2012). Agile Project Management in Easy Steps. In Easy Steps.
Information Technology Project Management - 4th Edition, Jack T. Marchewka, John Wiley and Sons, Inc.
A Guide to the Project Management Body of Knowledge Pmbok Guide - 5th Edition: Project Management Institute: Books. (n.d.).
Authors:
Reference: null
Year:
Title: Moss, L., Atre, S. (2003) Business Intelligence Roadmap - the Complete Project Lifecycle for Decision-Support Applications. Addison-Wesley.
DAMA International (2017) DAMA-DMBOK - Data Management Body of Knowledge (2nd Edition). Technics Publications.
Carroll, J. (2012). Effective Project Management in Easy Steps. In Easy Steps.
Meredith, J. R., Mantel, S. J., & Jr. (2011). Project Management: A Managerial Approach. John Wiley & Sons.
Materiais diversos fornecidos pelos docentes da UC.
Authors:
Reference: null
Year:
Data Warehouse Systems II
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).
Title: - Kimball, R., Caserta, J. (2004). The Data Warehouse ETL Toolkit - Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, John Wiley & Sons.
Authors:
Reference: null
Year:
Title: - 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.
- L. Corr, J. Stagnitto (2011) Agile Data Warehouse Design - Collaborative Dimensional Modeling, from Whiteboard to Star Schema. DecisionOne Press, UK.
- 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
- C. Adamson (2010) Star Schema: the complete reference. McGraw-Hill, USA
Authors:
Reference: null
Year:
Text Mining
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.
Title: 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/
Authors:
Reference: null
Year:
Title: 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/
Authors:
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Dissertation in Integrated Decision Support Systems
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.
Title: - 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.
Authors:
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Title: - 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
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Recommended optative
Digital transformation technologies with the following Curricular Units:
03691 | Blockchain
03557 | Business Process Management
03746 | Internet of Things Laboratory
04401 | Disruptive Tecnologies
03579 | IoT for Smart Cities
Internet of Things (4 of the following Curricular Units):
03209 | Fundamentals of Data Science
03746 | Internet of Things Laboratory
03691 | Blockchain
03579 | IoT for Smart Cities
04401 | Disruptive Technologies
Computational Data Science:
03363 | Computational Intelligence and Optimization
02864 | Big Data Algorithms
02870 | Text Mining (required)
03209 | Fundamentals of Data Science
Recommended options *
1st Semester (1St year a free Course Unit and 2nd year have two Specialization Curricular Units):
02864 | Big Data Algorithms
03203 | Fundamentals of Information Technology Governance
03746 | Internet of Things Laboratory
03209 | Fundamentals of Data Science
02198 | Computational Language Processing
03363 | Computational Intelligence and Optimization
03579 | IoT for Smart Cities
2nd Semester (choose two specialization courses):
03691 | Blockchain
03557 | Business Process Management
04401 | Disruptive Technologies
Notes:
When choosing the Optional Curricular Units, students must ensure that there is no overlap with the compulsory Curricular Units.
Optional Curricular Unit will only be held if they achieve a minimum number of enrollments.
There is an enrollment limit for each Curricular Unit.
Objectives
The Master's in Integrated Business Intelligence Systems (MSIAD) has as its mission to provide the market with professionals able to manage, define, implement and successfully use decision-making support systems, duly integrated into the management of organizational information. The general objectives defined for the MSc are:
- to provide advanced and comprehensive training in different types of decision support systems, integrated into an organization's global information systems architecture.
- to promote the integration and synergy of knowledge in the areas of information technology, management and quantitative methods;
- to generate research in the scientific area of Business Intelligence, fostering the sharing of knowledge, methods and results between companies and universities.
The classes will allow students to acquire the following knowledge and skills:
- comprehensive theoretical and practical training in different types of Decision Support Systems (DSS) integrated into the global architecture of an organization's information systems, including data-based, knowledge-based and model-based DSSs;
- designing, implementing and exploring of Data Warehouse (DW) and Business Intelligence systems;
- dimensional modeling techniques for DW;
- knowledge and expertise in the specification, development and management of DW - ETL (Extract, Transform, and Load) systems.
- knowledge of strategic information systems to manage organizations and support strategic decision-making;
- designing and developing strategic information and performance management systems, applying the Balanced Scorecard and ABC/ABM approaches;
- applying data analysis techniques to specific problems;
- developing systems for extracting knowledge from business data using Data Mining techniques;
- knowledge of the main methodologies for risk management in organizations;
- understanding the decision-making process in Marketing;
- designing and developing applications for Customer Intelligence (Analytical CRM);
- knowledge about the main models used in the management of IS/IT (information systems/information technology) projects, traditional and responsive;
- ability to plan, monitor, control and complete the management of a project in Business Intelligence;
- knowledge about the necessary technologies for the development of Text Mining processes;
- ability to apply appropriate methods and algorithms to resolve problems in the areas of computational language processing, text classification and document representation;
- understanding of the current trends and challenges in Big Data Analytics;
- ability to apply multi-criteria analysis to specific problems.
The program's learning objectives are achieved through the specific objectives of each curricular unit, which can be found in their respective CUFs (Curricular Unit Form). Each CUF also details the methodologies of used for evaluating each specific objective, which together determine the degree of course completion.