Description

Theory/Practice
1

Laboratory
3

Instructors

Carlos Ferreira

Contents

SY1-Introduction to Data Analysis and Knowledge Management
SY2-Data Preparation
2.1 Data Characterization
2.2 Data Exploration
2.3 Data Preprocessing
SY3-Predictive Models
3.1. Predictive Model Evaluation
3.2. Distance-Based Methods
3.3. Probabilistic Methods
3.4. Search based methods
3.5. Optimization based methods
3.6. Multiple Models
SY4 - Clustering
Partition and hierarchical algorithms.
Internal and external evaluation measures.
SY5-Descriptive Models
Evaluation metrics
 

Learning Outcomes

The student after attending this course will be able to:
LO1- Know and understand the process of exploratory analysis and knowledge extraction from data;
LO2- Know and understand the methods/techniques for data preparation;
LO3- Know and understand the different predictive methods/models for Data Knowledge Extraction;
LO4- Know how to translate business objectives into data-driven discovery objectives.