Base Knowledge
NA
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 anddiscussed with the class, followed by demonstrative examples;
2 – Experimental method: an active method where the student develops knowledge through problem-solving and thedevelopment of individual laboratory projects or group dynamics.
Learning Results
At the end of the curricular period of this UC, the student must:
– Identify the main concepts of data science applied to management – (LO1)
– Implement applications in the field of data science – (LO2)
– Using methods/algorithms in new data science problems and evaluating the results – (LO3)
– Evaluate and interpret the work carried out in the field of data science for business – (LO4)
– Use the concepts and tools analyzed and discussed in class in future projects and in the labor market – (LO5)
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
Grading Methods
- - Exam - 75.0%
- - Individual and/or Group Work - 25.0%
- - a) practical assignements - 50.0%
- - Exam - 50.0%
Internship(s)
NAO
Bibliography
Jung, A. (2021). Machine Learning: The Basics. Springer Nature.
Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python.
O’Reilly Media.
Gama J. (2017); Extração de Conhecimento de Dados Data Mining, Silabo
Taddy, M. (2019). Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions. McGraw Hill Professional.