Description

Theory
1

Theory/Practice
1

Laboratory
3

Instructors

Fátima Rodrigues

Contents

P1. Introduction to Data Science
- Process of knowledge extraction from data
- Methodologies
P2. Data Pre-Processing
- Exploratory data analysis
- Data transformation
- Feature selection
- Data balancing
P3. Model evaluation
- Sampling methods
- Model evaluation metrics
P4. Predictive Models
- Distance-based methods, probabilistic methods, search-based methods, optimisation-based methods
P5. Multiple Predictive Models
- Ensemble voting, stacking, bagging, and boosting
P6. Time Series
- Handling missing data, interpolation methods
- Time series decomposition
- Prediction with Statistical methods, Machine Learning methods, and optimisation-based methods.

Learning Outcomes

By the end of this course, the student must be able to:
CO1. Explain the process of knowledge discovery from data and its various phases. (Bloom Level (BL): 6/6)
CO2. Translate business goals into data mining goals. (BL 5/6)
CO3. Select, prepare, and explore the data according to the objectives of knowledge discovery and the algorithms to be applied to the data (BL 5/6)
CO4. Identify the most current and efficient data mining algorithms, explain how they work when applied to data sets, and know how to combine different DM algorithms in order to obtain better results. (BL 4/6)
CO5. Evaluate data models with the most appropriate measures (BL 6/6)
CO6. Develop a data-mining project for a specific problem using an adequate methodology, write a project report, present and defend it. (BL 6/6)