Mathematics for Data Science Probability Programming in R Statistical Inference Advanced Programming Multivariate Analysis Regression Models

Become familiar with different analytical tools, based on data, to make business decisions Capacity to develop skills to analyze and find relationships between many variables/features Know how to evaluate supervised-learning models Develop skills to classify observations based on probabilistic learning and machine learning tools Handle the R and Python languages for statistical-learning tools

1. Introduction to Statistical Learning 2. Performance Evaluation of Learning Models 3. Bayesian Learning 4. Bayes Rule and Cost-Sensitive Learning 5. k-Nearest Neighbors (k-NN) 6. Support Vector Machines 7. Decision Trees and Random Forests 8. Neural Networks

Lectures (50% of the sessions): the contents of the course will be introduced, explained and illustrated with examples. Teaching materials will be provided on Aula Global. Computer Labs (50% of the sessions): Examples and cases studies with the R and Python languages.