Predictive Methods of Data Mining
The curricular unit of data mining primary goal is to provide students with a fundamental understanding of Predictive Analytics as it relates to improving business performance.
This course will cover the basics of predictive analytics and modelling data to determine which algorithms to use and 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 neighbour, 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.
Roberto André Pereira Henriques
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
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.
Regular examination period (1st examination period)
- Exam (60%)
- Project (40%)
Resit examination period (2nd examination period)
- Exam (60%)
- Project (40%)
Minimum grade of 8.0 (in 20) for the exam
Minimum grade of 4.0 (in 20) for the project
The course is divided in the following learning units (LU):
LU1. Introduction to predictive modeling
LU2. Data pre-processing
LU3. Introduction to classifiers
- Linear and Logistic regression
- Bayesian learning systems
- Instance-based learning and classification
- Regression and classification trees
- Ensemble classifiers CUC4. Predictive models evaluation
- Neural networks and Deep Learning Neural Networks
LU4. Predictive models evaluation
LU5. Develop a projet using SAS EMiner
Programs where the course is taught:
- Specialization in Information Analysis and Management
- Specialization in Risk Analysis and Management
- Specialization in Knowledge Management and Business Intelligence
- Specialization in Information Systems and Technologies Management
- Specialization in Marketing Intelligence
- Specialization in Marketing Research and CRM
- Specialization in Knowledge Management and Business Intelligence – Working Hours Format
- Specialization in Information Systems and Technologies Management - Working Hours Format
- Specialization in Marketing Intelligence - Working Hours Format
- Post-Graduation in Information Analysis and Management
- Post-Graduation Risk Analysis and Management
- PostGraduate in Smart Cities
- PostGraduate in Digital Enterprise Management
- PostGraduate in Information Management and Business Intelligence in Healthcare
- Post-Graduation in Knowledge Management and Business Intelligence
- Post-Graduation Information Systems and Technologies Management
- PostGraduate in Enterprise Information Systems