Base Knowledge
Basic notions of statistics are recommended
Teaching Methodologies
The following teaching methodologies are used in this course:
1. Expository method: explanatory method where theoretical foundations and concepts are presented by the teacher and discussed with the class. Concepts and information will be presented to students through, for example, slide presentations or oral discussions. It will be used in classes to structure and outline the information.
2. Demonstrative method: based on the example given by the teacher of a technical or practical operation that one wishes to be learned. It focuses on how a given operation is carried out, highlighting the most appropriate techniques, tools and equipment. It will be used, for example, in practical and laboratory classes.
3. Interrogative method: process based on verbal interactions, under the direction of the teacher, adopting the format of questions and answers. It allows for greater dynamics in the classroom and consolidates learning. It will be used, for example, to remember elements of previous classes and in revisions of the lectured content.
4. Active methods: pedagogical techniques will be used in which the student is the center of the learning process, being an active participant and involved in his own training. The teacher assumes the role of facilitator, stimulating critical thinking, collaboration, creativity and student autonomy. They will be applied in classes to achieve a dynamic and more lasting learning environment.
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
- - Exam - 75.0%
- - Individual and/or Group Work - 25.0%
- - 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.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms. MIT press.
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.
Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT press.