Data analysis

The following teaching methodologies are used in this curricular unit:

1       – Expository method: an explanatory method were theoretical foundations and concepts are presented by theteacher and discussed with the class, followed by demonstrative examples;

– Experimental method: an active method were the student develops the knowledge through the use of problemsolving and project development, individually and in group dynamics

It is expected that by the end of the course each student is entitled to:

1. Understand and apply the techniques for exploratory and intelligent data analysis;

2. Project and develop approaches for data transformation and data analysis using large databases;

3. Extract, clean and transform datasets.

1. Data Mining: Concepts, Objectives, Knowledge Extraction from Databases, Data Cleaning and Transformation,

Machine Learning in Data Mining, Data Mining Learning Techniques, Algorithms Evaluation, Models Evaluation; 2. Data Warehousing: Data Warehouse Architecture, Multidimensional Model, Data Staging Design and Development (Extraction, Transform and Load).

• - one individual working assignment (50%) - 50.0%
• - one individual development project (50%) - 50.0%

• - one development group project (50%) - 50.0%
• - one individual research assignment (50%) - 50.0%

NAO

“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