Data Science and Machine Learning
Objectives
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
Code
400088
Credits
7.5
Responsible teacher
Roberto André Pereira Henriques
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
None
Bibliography
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