Data Mining II


The curricular unit of data mining has as primary goal to allow students to gain a fundamental understanding of the art and science of Predictive Analytics as it relates to improving business performance.

This course will cover the basics of predictive analytics and modeling data to determine which algorithms to use. It will also allow students to understand the similarities and differences and which options affect the models most.

Topics covered include predictive analytics algorithms for supervised learning, including decision trees, neural networks, k-nearest neighbor, support vector machines, and model ensembles.

At the end of the course, participants will be able to use these skills to produce a fully processed data set compatible with building powerful predictive models that can be deployed to increase profitability.

General characterization





Responsible teacher


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Data Mining I is not a prerequisite.


Mitchell, T., (1997) ¿Machine Learning¿, McGraw Hill.; Berry, M.J.A. and G.S. Linoff, ¿Data Mining Techniques; for marketing, sales and customer support¿. 1997, John Wiley & Sons.; Hand, D. J., Mannila, H., Smyth, P. (2001) ¿Principles of Data Mining (Adaptive Computation and Machine Learning)¿, MIT Press; 0; 0

Teaching method

The course is mainly based on lecture and practical classes. The practical sessions include exposure of concepts and methodologies, sample resolution, discussion and interpretation of results. Practical work, which is very significant in this course is done by students outside the classroom, but is evaluated.

Evaluation method

The evaluation is done through homework (20% of final grade), a practical group (20%), and one final exam (60%)

Subject matter

The course is organized in seven Learning Units (UA):
UA1. Introduction to forecasting methods in Data Mining
UA2. Data pre-processing and error estimates
UA3. Decision theory and Bayesian
UA4 Learning and classification systems
UA5 Decision trees
UA6. Neural Networks
UA7. Ensambles