Teaching Methodologies
In this curricular unit are used the following teaching methodologies:
1. Exhibition method: explanatory method where theoretical foundations and concepts are presented by the teacher and discussed with the class, followed by demonstrative examples;
2. Experimental method: active method where the student develops knowledge through problem solving and the development of individual laboratory projects or group dynamics.
Learning Results
It is expected that at the end of the curricular unit the student will be qualified to:
1. Understand to apply the techniques of intelligent data exploration and analysis;
2. Design and develop approaches for data transformation and analysis using large databases;
3. Extract, clean and transform datasets.
Program
1. Data Mining; Concepts, Objectives, Knowledge Extraction of Databases, Cleaning and Data Transformation, Learning in Data Mining, Algorithm Evaluation, Model Evaluation, Data Mining Applications;
2. Data Warehousing: Data Warehouse Architecture, Multidimensional Model, Design and Implementation of the Internship Area (Extraction, Transformation and Loading).
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
“The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling”, 3rd Edition; Ralph Kimball, Margy Ross; J. Wiley & Sons; ISBN 0471200247; 2013
“Data Warehousing – Conceitos e Modelos”; Carlos Caldeira; Edições Silabo; ISBN 9789726186960; 2012
“Data Mining: Practical Machine Learning Tools and Techniques”, 3rd Edition; Ian H. Witten, Eibe Frank; Morgan Kaufman; ISBN 0123748569; 2011
“Data Mining: Concepts and Techniques”, 3rd Edition; Jiawei Han, Micheline Kamber, Jian Pei; Morgan Kaufmann; ISBN 0123814790; 2011
“Análise de Dados para Ciências Sociais”, 6.º Edição; Maria Pestana, João Gageiro; Edições Sílabo; ISBN 9789726187752; 2014
“Extracção de conhecimento de Dados”; João Gama et al; Edições Sílabo; ISBN 9789726186984; 2012