P1. Writing the introduction and abstract (resumo);
P1.1. Definition of a research problem and goals;
P2. Writing the literature review;
P3. Writing the methodology
P4. Writing the results and its discussion
P5. Writing conclusions
P5.1. Contributions/Implications in academic and practical terms
P5.2. Limitations and new research paths
P6. Communicate orally the synthesis

Assessment throughout semesters
1) Written presentation of the dissertation (80%)
2) Oral presentation with the synthesis of the dissertation followed by a public defense with a jury (20%)

Minimum classification: 10 points; scale: 0 - 20 points

Mandatory

Bougie, R. & Sekaran, U. (2020) Research Methods for Business, 8th Edition, Wiley. ISBN: 978-1119663706.

N. Bui, I. (2019). How to Write a Master′s Thesis, 3rd Edition, Sage. ISBN: 978-1506336091.

Oliveira, L. A. (2011). Dissertação e Tese em Ciência e Tecnologia Segundo Bolonha. Lisboa: LIDEL. ISBN: 978-9727577422.

Fisher, C. (2007). Researching and writing a dissertation: A guidebook for business students. 3rd Edition, Pearson. ISBN: 978-0273723431.

Definida pelo orientador / Defined by supervisor

Optional

Provost, F., & Fawcett, T. (2013). Data Science for Business Fundamental principles of data mining and data-analytic thinking. Sebastopol, CA: O'Reilly.

Pidd, M. (2003). Tools for thinking: Modelling in Management Science. West Sussex: Wiley.

Brennan, K. (2009). A Guide to the Business Analysis Body of Knowledge (BABOK Guide). IIBA.

Øvretveit, J. (2008). Writing a scientific publication for a management journal. Journal of Health Organization and Management, 22, 2, 189-206.

P1. Writing the introduction and abstract (resumo);
P2. Definition of the business problem and diagnosis of organizational environment;
P3. Definition of the project goals;
P4. Applied literature review;
P5. Defining the analytical objectives and monitoring metrics;
P6. Data understanding and preparation;
P7. Data analysis methods (modelling) and evaluation;
P8. Writing conclusions and defining new projects paths;
P9. Evaluation of impacts and possibilities of control of results.

- Written presentation of the thesis (80%)
- Oral presentation with the synthesis of the project followed by a public defense with a jury (20%)

Mandatory

Camm, J., Cochran, J., Fry, M., Ohlmann, J., Anderson, D., Sweeney, D., & Williams, T. (2015). Essentials of Business Analytics, Cengage Learning.
Uma Sekaran e Bougie Roger (2010) Research Methods for Business, 5ª edição, John Wiley and Sons
Oliveira, Luís Adriano (2011). Dissertação e Tese em Ciência e Tecnologia Segundo Bolonha. Lisboa: LIDEL
Laursen, Gert & Thorlund, Jesper (2010) Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, Wiley.
Fisher, C. (2007). Researching and writing a dissertation: A guidebook for business students. Essex: Prentice Hall
Bell, Judith (2005). Doing Your Research Project: a guide for first-time researchers in education and social science. 4th ed. Buckingham: Open University Press.
Definida pelo orientador / Defined by supervisor

Optional

Provost, F., & Fawcett, T. (2013). Data Science for Business Fundamental principles of data mining and data-analytic thinking. Sebastopol, CA: O?Reilly.
Pidd, M. (2003). Tools for thinking: Modelling in Mangement Science. West Sussex: Wiley.
Brennan, K. (2009). A Guide to the Business Analysis Body of Knowledge (BABOK Guide). IIBA.
Øvretveit, J. (2008). Writing a scientific publication for a management journal. Journal of Health Organization and Management, 22, 2, 189-206.

P.1 The stages of research
P1.1.Types of thesis
P1.2. Identifying the research problems
P1.3. Planning the research stages
P2. Doing a literature review and identifying the main literature sources
P3. Types and stages of empirical work in business analytics
P3.1. Identify methodologies
P3.2. Identify data, analytical tools and techniques to solve the research problem
P4. Development and formal presentation of the research project and scientific article
P4.1. Tutorial and individual or small group monitoring of the development and improvement of research work

Evaluation throughout the semesters:
1) Thesis project (60%)
2) Active participation in class (10%) - 5 classroom activities completed
3) Writing, individually, or in a group, a scientific article and respective oral and digital presentation at a scientific conference (10% theoretical sections + 10% empirical sections + 10% complete article submitted and presented at scientific conference, with the supervisor agreement)

Requires a min. grade of 10 pts. in elements 1) and 2), article submission according to the conference’s requirements, attendance to classes of at least 2/3, and a min. of 10 pts. in the final classification.

Scale: 0-20 points.

This course does not have a final exam.

Mandatory

Dhar, R. & Gastel, B., How to Write and Publish a Scientific Paper, 2016, 8th Edition, Greenwood.

Kitchenham, B., Procedures for Performing Systematic Reviews, Joint Technical Report TR/SE-0401, 2004, Keele University.

Peffers, K., Tuunanen, T., Gengler, C., Rossi, M., Hui, W., Virtanen, V., & Bragge, J., The Design Science Research Process: A Model for Producing and Presenting Information Systems Research,, 2006, First International Conference on Design Science Research in Information Systems and Technology (DESRIST 2006).

Saunders, M., Lewis, P., & Thornhill, A., Research Methods for Business Students, 2019, 8th Edition, Pearson.

vom Brocke, J., Hevner, A., & Maedche, A. (Eds.), Design Science Research. Cases, 2020, Springer.

Passos, F., Laureano, R. & Passos, M., Predictive Model for Heart Failure Decompensation: A Systematic Literature Review, 2024, 19th Iberian Conference on Information Systems and Technologies (CISTI).

Optional

Bhattacherjee, A., Social Science Research: Principles, Methods, and Practices, 2012, 2nd edition, University of South Florida, Scholar Commons.

Flick, Uwe, An Introduction to Qualitative Research, 2023, 7th Edition, Sage Publications.

IBS, Regras de Elaboração de Dissertação ou Trabalho de Projeto de Mestrado, 2020, Iscte-Business School.

ISCTE-IUL, Ética na Investigação: Melhores Práticas, melhor Ciência, 2016, ISCTE-IUL.

Roger, B. & Sekaran, U., Research Methods for Business,, 2020, 8ª edição, Wiley.

PC1. Big Data: Introduction, challenges, trends and applications
PC2. Big Data characterization: The V's of Big Data
PC3. Big data technologies
PC4. Stream Analysis and analytical models for Big Data
PC5. Business cases about Big Data problems and analytical solutions

1. Assessment throughout the semester: a) Group assignment with presentation (50%). (LG 2, 3); b) Individual test (50%). (LG 1, 3).
Approval by assessment throughout the semester requires:
- students attendance of at least 75% of classes;
- minimum grade of 7,5 in the individual test and the group assignment;
- minimum final grade of 10.

2. Assessment by exam: Individual test (50%) and Individual assignment with presentation (50%).
Approval by exam requires:
- minimum grade of 10 for each of the evaluation elements;
- minimum final classification of 10.

Tests and exams will be taken in person, whether or not digital software/platforms are used.

The use of calculators, cell phones, smartwatches, or any other electronic equipment is not permitted during written tests. Illegal use of such equipment will be sanctioned under the Student Disciplinary Regulations.

The teachers responsible reserve the right to conduct oral exams when necessary.

Mandatory

Bahga, A. & Madisetti, V. (2016). Big Data Science & Analytics: A Hands-On Approach. VPT.
Li, K. C., Jiang, H., Yang, L. T., & Cuzzocrea, A. (2015). Big data: Algorithms, analytics, and applications. CRC Press.
Leskovec, J., Rajaraman, A. & Ullman, J.D. (2020). Mining of Massive Datasets. 3rd Edition, Cambridge University Press.
Melcher, K. & Silipo, R. (2020) Codeless Deep Learning with KNIME. Packt Publishing.

Optional

Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. & Silipo, R. (2020). Guide to Intelligent Data Science: How to Intelligently Make Use of Real Data. 2nd Edition, Springer International Publishing.
Marz, N. & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems. Manning Publications.
Ryza, S., Laserson, U., Owen, S., & Wills, J. (2015). Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O'Reilly Media.

P.1. Business analytics in the organizational context
P1.1. Concepts
P1.2. Framework to implement
P1.3. CRISP-DM methodology
P1.4. Analytics maturity
P1.5. Other relevant topics (e.g. ethics, sustainability)
P2. Business analytics applications
P2.1. Data problems
P2.2. Business intelligence problems
P2.3. Analytics problems
P3. Computer analytical tools
P3.1 Business Intelligence
P3.2 Data analytics and artificial intelligence
P3.3 Other tools

Assessment throughout the semester:
a) Successful completion of two e-learning courses (7,5% + 7,5%) - LG 1, 2
b) Individual work with oral and digital presentation (50%) - LO 1, 2, 3
b) Group work with possible discussion (35%) - LO 2, 3

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

Scale: 0-20 points

Given the practical nature of the content of the course and the constant interaction with professionals, no exam is scheduled.

Mandatory

Camm, J., Cochran, J., Fry, M., Ohlmann, J. (2024). Business Analytics, 5th Edition, Cengage. ISBN: 978-0357902202.

Laursen, Gert H. N. & Thorlund, Jesper (2017)., Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, 2nd Edition, Wiley. ISBN: 978-1119298588.

Optional

Brennan, K. (2015). A Guide to the Business Analysis Body of Knowledge (BABOK Guide), V3, Int'l Institute of Business Analysis (IIBA).
Isson, J. P. & Harriott, J. (eds) (2013). Win with advanced business analytics: creating business value from your data, John Wiley & Sons,
Palmatier, Robert & Sridhar, Shrihari (2021). Marketing Strategy: Based on First Principles and Data Analytics, 2nd Edition, Bloomsbury Academic.
Roberts, Terisa & Tonna, Stephan J. (2022). Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning, Wiley. ISBN 978-1119824930.
Santos, M. & Ramos, I. (2009). Business Intelligence: Tecnologias da Informação na Gestão de Conhecimento, 2ª Edição, FCA.
Schniederjans, M. J., Schniederjans, D. G., & Starkey, C. M. (2014). Business analytics principles, concepts, and application what, why, and how, Pearson.
Sharda, Ramesh, Dursun Delen & Efraim Turban (2015). Business Intelligence and Analytics: Systems for Decision Support, 10th Edition, Pearson.
Siegel, E. (2016). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Wiley.
Venkatesan, R, Farris, P. Wilcox R. (2014). Cutting Edge Marketing Analytics: Real World Cases and Datasets for Hands On Learning, Pearson/FT Press.

A. Data management background in the data-science universe
B. Relational Schemas Design
1. Relations and primary keys
2. Foreign keys and rules of integrity;
3. Critical analysis and construction of a relational model;

C. Language S.Q.L
1 Simple Interrogations;
2 Aggregation and Grouping Functions;
3 Chained Interrogations;
4. Creation of Views

D. Optimization

E. Dimensional Model
1. Dimensional model design
2. Dimensional vs relational model
3. ETL & data quality

F. Semi-structured data

Assessment by exam (1st Period, 2nd Period, and Special Period):
* Written test (100%)
- Approval: grade >= 10 points.

Assessment throughout the semester:
* Group work with discussion - phased delivery throughout the semester and discussion at the end of the semester (50%);
* Individual written test - 1st Period date (50%).
- Approval: Final classification >=10 points; and Individual written test >=8 points.
- Failure to attend the discussion implies canceling the group work as an assessment item.
- The final grades of group work will depend on each student's performance in the discussion and may vary between 0 (zero) and 20 points.

Mandatory

Ramakrishnan , Raghu; Gehrke, Johannes. Database Management Systems. 3rd Edition. McGrawHill. 2003
Perreira, J. Tecnologia de Base de Dados" FCA Editora de Informática, 1998
Damas, L. SQL - Structured Query Language " FCA Editora de Informática, 2005
Kimball R, Ross M. The Data Warehouse Toolkit. 3rd ed. John Wiley & Sons; 2013.
Kimball R, Caserta J, ?The Data Warehouse ETL Toolkit?, Wiley, 2004

Optional

Gorelik, Alex. The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science. 1st Edition, O'Reilly, 2019

S1. Introduction to descriptive and diagnostic analysis
1.1. Data types
1.2. Summarisation techniques for univariate and bivariate data
1.3. Assessing data quality: missing responses and outliers
S2. Descriptive and diagnostic techniques for multivariate data
2.1. Dimensionality reduction: Principal component analysis (PCA)
2.2. Clustering (e.g., hierarchical agglomerative method and K-means)
2.3. Association rules (market basket analysis; e.g., apriori and sequential algorithms)
S3. Practical applications of descriptive and diagnostic techniques with real data, using IBM SPSS Modeler and IBM SPSS Statistics (or other software)

Assessment throughout the semester
a) Individual written test (60%);
b) Group applied project (40%), with possible digital presentation or discussion.
Approval requirements:
a) Minimum mark of 7.5 out of 20 in each component;
b) Minimum final mark of 10 out of 20;
c) Attendance of at least two-thirds of the scheduled classes.

Assessment by exam (first, second and third sitting)
Individual applied project (40%), with possible digital presentation or discussion, and individual written exam (60%), both requiring a minimum mark of 7.5 out of 20. Minimum final mark: 10 out of 20.

The written test or exams are closed-book.

Mandatory

1. Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). Sage: Los Angeles.
2. Camm, J., Cochran, J., Fry, M., and Ohlmann, J., (2021). Business Analytics, (4th Ed.). Cengage Learning.
3. Larose, D. & Larose, C. (2015). Data Mining and Predictive Analytics (2nd Ed.). Wiley Series on Methods and Applications in Data Mining, Wiley. ISBN: 978-1-118-11619-7.
4. Wendler, T. & Gröttrup, S. (2021). Data Mining with SPSS Modeler: Theory, Exercises and Solutions (2nd edition). Springer. ISBN: 978-3030543372.

Optional

1. Marôco, J. (2021). Análise Estatística com o SPSS Statistics (8ª edição). ISBN: 9789899676374.
2. Laureano, R. e Botelho, M.C. (2017). IBM SPSS Statistics: O Meu Manual de Consulta Rápida (3ª ed.). Edições Sílabo, ISBN: 978-972-618-886-5.
3. Laureano, R. (2020). Testes de Hipóteses e Regressão - O Meu Manual de Consulta Rápida (2ª edição). Edições Sílabo. ISBN: 978-989-561-051-8.

P1. Classification techniques:
P1.1. Decision trees and propositional rules
P1.2 Neural networks: the backpropagation algorithm
P1.3 Other algorithms for classification problems
P2.1. Linear regression
P2.2. Decision trees
P2.3 Neural networks: the backpropagation algorithm
P2.4. Other algorithms for regression problems
P3. Applications of classification and regression with real data, using IBM SPSS Modeler and IBM SPSS Statistics; or other

1) Assessment throughout the semester:
a) Written test (50%) - LO 1, 2
b) A group project with digital presentation (50%) and possible discussion (LO 1, 2. 3)

Requires a minimum grade of 7.5 points in each element, attendance to classes of at least 2/3, and a minimum of 10 points in the final classification.

2) Exam (all periods): a one week project with discussion and digital presentation (50%) and written test (50%), requiring minimum 10 points in each assignment to get approval.

Scale: 0-20 points.

Mandatory

Larose, D. & Larose, C. (2015). Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining), 2nd edition, Wiley. ISBN: 978-1-118-11619-7.

Lopez, C. (2022). Machine Learning. Supervised Learning with SPSS Modeler, Scientific Books. ISBN: 978-1471018046.

Quinn, J. (2020). The Insider' Guide to Predictive Analytics, Smart Vision Europe. ISBN: 978-1838058104.

Wendler, T. & Gröttrup, S. (2021). Data Mining with SPSS Modeler: Theory, Exercises and Solutions, 2nd edition, Springer. ISBN: 978-3030543372.

Witten, I., Frank, E., Hall, M. & Pal, C. (2011). Data Mining: Practical Machine Learning Tools and Techniques, 4th edition, Morgan Kaufmann. ISBN: 978-0128042915.

Optional

Gama, J., Carvalho, A., Faceli, K., Lorena, A., & Oliveira, M. (2012). Extração de Conhecimento de Dados: Data Mining, Edições Sílabo.

Hair, J.F., Black, W.C., Babin, B.J. & Anderson, R.E. (2010). Multivariate Data Analysis, 7th edition, Prentice Hall.

Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The Elements of Statistical Learning, Vol. 2, Springer.

Laureano, R. (2020). Testes de Hipóteses e Regressão: o Meu Manual de Consulta Rápida, Edições Sílabo.
McCormick, K., Abbott, D., Brown, M., Khabaza. T., & Mutchler, S. (2013). IBM SPSS Modeler Cookbook, Packt

Rocha, M., Cortez, P. & Neves, J. (2008). Análise Inteligente de Dados - Algoritmos e Implementação em Java, FCA.

Salcedo, J. & McCormick, K. (2017). IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions, Packt.

Santos, M. & Ramos, I. (2009). Business Intelligence: Tecnologias da Informação na Gestão de Conhecimento, 2ª Edição, FCA.

Vasconcelos, J., & Barão, A. (2017). Ciência dos Dados nas Organizações: Aplicações em Python, FCA.

Programmatic Contents (PC):
PC1. Mathematical optimization models and techniques
1.1 Mathematical optimization models
1.2 Applications in Management
1.3 Resolution using software
1.4 Interpreting results making prescriptive recommendations
PC2. Heuristic optimization techniques
2.1 Introduction to heuristic optimization
2.2 Applications in Management
2.3 Resolution using software
2.4 Interpreting results making prescriptive recommendations
PC3. Other prescriptive models and techniques for Management
3.1 Simulation
3.2 Time Series Forecasting

Assessment throughout the semester or assessment by exam:
1. ASSESSMENT THROUGHOUT THE SEMESTER:
a) Written test: i) weight of 60%; ii) minimum classification of 8.5;
b) Group project: i) weight of 40%; ii) Groups of 4 students; iii) with presentation and oral discussion;
c) Attendance of at least 2/3 of the classes taught;
d) Approval: minimum weighted average of 9.5.
2. ASSESSMENT BY EXAM (1st and 2nd Season):
a) Written test: i) weight of 60%; ii) minimum classification of 8.5;
b) Individual project: i) weight of 40%; ii) with presentation and oral discussion;
c) Approval: minimum weighted average of 9.5.
In both assessment methods, an oral discussion may be required.
Scale: 0-20 points.

Mandatory

* Evans, J. (2021). Business Analytics, 3rd Ed. Global Edition. Pearson.
* Ragsdale, C.T. (2017). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics, 8th Ed. Cengage Learning.
* Murty, K. G. (2003). Optimization Models For Decision Making: Volume 1. Web-book. http://www-personal.umich.edu/~murty/books/opti_model/

Optional

* Greasley, A. (2019) Simulating Business Processes for Descriptive, Predictive and Prescriptive Analytics, De Gruyter.
* Borshchev, A. (2015). The Big Book of Simulation Modeling. Multimethod Modeling with AnyLogic 6.
* Taha, H. A. (2016). Operations Research: An Introduction, 10th Ed. Pearson.
* Korte, B. and Vygen, J. (2012). Combinatorial Optimization: Theory and Algorithms, 5th edition. Springer.
* Pinedo, M.L. (2012). Scheduling: Theory, Algorithms, and Systems, 4th edition. Springer.
* Cook, J.W. (2014). In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation, 3rd Ed. Princeton University Press.

P1. Fundamentals of data architecture
P2. Data storage and retrieval
P3. Data integration
P4. Data analysis, modelling and visualisation
P5. Data security and quality
P6. Architectures with emerging technologies
P7. Problem identification
P8. Justification of proposals
P9. Identifying risks and benefits
P10. Definition of technological requirements
P11. Communication business cases

Students’ evaluation throughout the semester is based on the following components:
a) 10% in accordance with student's attendance at classes;
b) 50% with group work, where students must demonstrate their ability to develop a business case, with collaborative work and brainstorming, through the recording of a video (pitch);
c) 40% with the individual delivery of the technological requirements of the business case and possible discussion.

Requires a minimum grade of 10 points in all itens.

Scale: 0-20 points.

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

Mandatory

Gatti, S. (2023). Project Finance in Theory and Practice: Designing, Structuring, and Financing Private and Public Projects. Academic Press. ISBN: 978-0323983600.

Inmon, B.; Levins, M.; Srivastava, R. (2021). Building the Data Lakehouse. Technics Publications. ISBN: 978-1634629669.

Kerzner, H. (2022). Project Management: A Systems Approach to Planning, Scheduling, and Controlling. 13th edition, Wiley. ISBN: 978-1119805373.

Koller, T., Goedhart, M., Wessels, D. (2020). Valuation: Measuring and Managing the Value of Companies. 7th edition, McKinsey & Company Inc. ISBN: 978-1119611868.

Serra, J. (2024). Deciphering Data Architectures. O'Reilly Media, Inc. ISBN: 978-1098150761.

Azure Architecture Center - Azure Architecture Center | Microsoft Learn.

Optional

Agrawal, D.; Selçuk Candan, K.; Li, W-S. (2011). Data Management in the Cloud: Challenges and Opportunities. Springer.
Erl, T.; Mahmood, Z.; Puttini, R. (2013). Cloud Computing: Concepts, Technology & Architecture. Prentice Hall.
Götze, U.; Northcott, D.; Schuster, P. (2015). Investment Appraisal: Methods and Models. Springer.
Kavis, M. (2014). Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS). Springer.
PMI (2017). The Standard for Portfolio Management. Project Management Institute.
Zhao, L. (2014). Cloud Data Management. Springer.

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

Option 1 (Continuous Assessment)

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

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

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

20-point scale.

Mandatory

Sharda, R., Delen, D., & Turban, E. (2018) Business intelligence, analytics, and data science: A managerial perspective. Pearson.
Pearce, J. & Robison, R. (2013) Strategic Management, 13th Edition, MacGraw-hill Higher Education.
Grant, R. (2018) Contemporary Strategy Analysis (10th edition), Wiley.
Barney, J. & Hesterley S. (2019) Strategic Management and Competitive Advantage: Concepts and Cases, 6th Edition, Pearson.

Optional

Gebauer, H. (2011) Exploring the contribution of management innovation to the evolution of dynamic capabilities. Industrial Marketing Management, 40(8), 1238-1250.
Eisenhardt, K. & Martin, J. (2000) Dynamic capabilities: What are they? Strategic Management Journal, 21(10/11), 1105-1121.
Breznik, L. & D. Hisrich, R. (2014) Dynamic capabilities vs. innovation capability: Are they related? Journal of Small Business and Enterprise Development, 21(3), 368-384.

PC1. Introduction to Text Mining
PC2. Tokenization, Dictionary Creation and Corpus Preparation
PC3. Clustering Methods for Text Mining
PC4. Classification Methods for Text Mining
PC5. Practical cases on using clustering methods in text mining for business
PC6. Application of Text Classification Cases Applied to Management: Sentiment Analysis.
PC7. Advanced NLP Topics.

1) Assessment throughout the semester:
a) Individual test (50%).
b) Group work 1 - Tools (Image, Video, Sound, or Text) (15%).
c) Group work 2 - Application/Project (Image, Video, Sound, or Text) (25%).
d) Class participation (10%).

Approval: a) min. 7.5 points in each of the assessment elements; b) minimum final classification. 10 values.

2) Assessment by exam (1st season): written test (100%), with a minimum grade of 10.
3) Assessment by exam (2nd season): written test (100%) with a minimum grade of 10.

Scale: 0-20 points.

Mandatory

Paaß and Giesselbach (2023). Foundation Models for Natural Language Processing. Springer.
Filipowska and Filipiak (2020). Big Data Management and Analytics - Introduction to Text Analytics. Springer.
Struhl, S. (2015). Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence (Marketing Science Series). Kogan Page.

Optional

Liu, Lin, Sun (2023). Representation Learning for Natural Language Processing. Springer.
Weiss, Indurkhya, Zhang, Damerau (2005). Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer
Srivastava, A.N. and Sahami, M. (2009). Text Mining: Classification, Clustering, and Applications. Chapman & Hall/CRC.
Feldman, R. and James Sanger, J. (2006). The Text Mining Handbookx\: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.