Class Data Analysis

The success of organizations depends on their ability to acquire information and transform it into knowledge that supports decision-making. Every day, a huge amount of data can be generated and stored and must be properly analyzed using statistical and computational techniques. Thus, it is pertinent that members of organizations have appropriate training in terms of multidimensional data analysis, taking advantage of new technologies, in order to learn to extract knowledge from data and develop and apply decision support and forecasting models. In this way, the curricular unit, by providing this training, contributes to the master's objective of qualifying the staff of organizations for the development and reinforcement of their competitiveness.

Not applicable

1) Introduction to multivariate data analysis 2) Exploratory analysis and data pre-processing 2.1) Data characterization 2.2) Data exploration 2.3) Data cleaning and transformation 2.4) Dimensionality reduction 3) Clustering 3.1) K-means algorithm 3.2) Silhouette coefficient 4) Prediction models 4.1) K-nearest neighbors algorithm 4.2) Decision trees 4.3) Artificial neural networks 4.4) Evaluation and selection of predictive models 4.5) Forecasting

Classes are of theoretical and practical nature, where the theoretical exposition of the syllabus is accompanied by the presentation of practical examples and exercises. Pedagogical innovation practices: Pedagogical practices will be mediated by Information and Communication Technologies, using computers and/or mobile devices. Students will be exposed to interactive educational resources, such as quizzes. Problem-based learning will be promoted, where the students, guided by the teacher, should identify problems and plan and carry out resolution paths. The aim is to improve the teaching and learning process, to provide students with the necessary skills for the challenges that humanity currently faces and to increase their motivation regarding the studies’ cycle.

- Gama, J., Carvalho, A., Faceli, K., Lorena, A. & Oliveira, M. (2017). Extração de Conhecimento de Dados - Data Mining (3ª Edição). Edições Sílabo. - Hastie, T., Tibshirani, R. & Friedman, J. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition). Springer. - Tan, P.-N., Steinbach, M., Karpatne, A. & Kumar, V. (2019) Introduction to Data Mining (2nd Edition). Pearson.