Data Science and Machine Learning


The curricular unit of Data Science and Machine Learning has as primary goal to allow students to gain fundamental understanding from Data Analytics concepts and Machine Learning methods allowing them to extract information and knowledge from large databases.

This field has been developing rapidly and has become central to various activities. Its contribution extends the scientific marketing research, constituting a very significant body of knowledge and is still expanding.

At the end of the course, participants will be able to understand the key concepts in data acquisition, preparation, exploration, and visualization. Also, machine learning theory combined with some practical scenarios on machine learning models will be covered. Practical application-oriented examples will be presented on how to build and derive insights from these models using Python.

General characterization





Responsible teacher

Roberto André Pereira Henriques


Weekly - Available soon

Total - Available soon

Teaching language

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




Teaching method

The course is based on a mix of theoretical lectures and practical lectures and tutorials. The theoretical sessions include the presentation of theoretical concepts and methodologies as well as application examples.

The main objective of the practical classes is to familiarize students with the software to perform the analysis and data explorations tasks.

Evaluation method

1st term exam

  • Exam (50%)
  • Project 1 (group) (35%)
  • Project 2 (individual) (15%)

2nd term exam

  • Exam (50%)
  • Project 1 (group) (35%)
  • Project 2 (individual) (15%)

Minimum grade of 8.0 (in 20) for the exams and projects

Subject matter

The course is divided into the following learning units (LU):

LU1. Introduction to Data Science

LU2. Data Science methodology and Introduction to Machine Learning

LU3. Working with Data: data pre-processing and Data visualization

LU4. Unsupervised Learning Models

  • Introduction to cluster analysis

LU5. Supervised Learning Models

  • Bayesian learning systems
  • learning and classification.
  • Regression and classification trees
  • Neural networks
  • Ensemble classifiers


Programs where the course is taught: