Base Knowledge
Basic notions of statistics are recommended
Teaching Methodologies
The following methodologies are used in this course:
1 – Expository method: an explanatory method where theoretical foundations and concepts are presented by the lecturer and discussed with the class, followed by demonstrative examples;
2 – Experimental method: an active method where the student develops knowledge through problem-solving and the development of individual projects or group dynamics.
Learning Results
At the end of the curricular period of this curricular unit, the student must:
– Identify the main concepts of data science applied to management
– Implement applications in the field of data science
– Using methods/algorithms in new data science problems and evaluating the results
– Evaluate and interpret the work carried out in the field of data science for business
– Use the concepts and tools analyzed and discussed in class in future projects and the labour market
Program
S1 – Introduction to data science concepts applied to management
S2 – CRISP-DM methodology
S3 – Data exploration
S4 – Data preprocessing
S5 – Feature engineering
S6 – Models for data science problems applied to management
S7 – Evaluation of models and interpretation of results
Curricular Unit Teachers
Grading Methods
- - Individual and/or Group Work - 25.0%
- - Exam - 75.0%
- - a) practical assignements - 50.0%
- - Exam - 50.0%
Internship(s)
NAO
Bibliography
Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python. O’Reilly Media.
Gama, J., Carvalho, A., Faceli, K., Lorena, A. C., & Oliveira, M. (2012). Extração de conhecimento de dados: data mining.
Grander, G., da Silva, L. F., & Santibañez Gonzalez, E. D. R. (2021). Big data as a value generator in decision support systems: A literature review. Revista de Gestão, 28(3), 205-222.
Jung, A. (2022). The Landscape of ML. In Machine Learning: The Basics (pp. 57-80). Singapore: Springer Nature Singapore.
Karkošková, S. (2023). Data governance model to enhance data quality in financial institutions. Information Systems Management, 40(1), 90-110.
Lu, J., Cairns, L., & Smith, L. (2021). Data science in the business environment: customer analytics case studies in SMEs. Journal of Modelling in Management, 16(2), 689-713.