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Data Mining

Code:

BINF022

Sigla:

DM

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2019/2020 - 2S

Ativa?	Yes
Unidade Responsável:	Matemática e Informática
Curso/CE Responsável:	Undergraduate in Bioinformatics

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
BINF	15	Study Plan	2	-	5	67,5	135

Língua de trabalho

Portuguese

Objetivos

Understand the main concepts related with Business Intelligence, namely DataWarehouse, ETL and Reporting Tools and the technological infrastructure;
Understand the importance of Business Analytics as a practice of iterative data exploration, to enable the decision making.
Understand the importance of Data Mining in organizations
Knowledge Discover in Databases through Data Mining techniques;
Understand the main concepts, methodologies and Data Mining techniques.

Resultados de aprendizagem e competências

Understand the main concepts related with Business Intelligence, namely DataWarehouse, ETL and Reporting Tools and the technological infrastructure;
Understand the importance of Business Analytics as a practice of iterative data exploration, to enable the decision making.
Understand the importance of Data Mining in organizations
Knowledge Discover in Databases through Data Mining techniques;
Understand the main concepts, methodologies and Data Mining techniques.

Modo de trabalho

Presencial

Programa

The challenges of Data Modeling and Analysis in Bioinformatics
Business Intelligence and Infrastructure
Fundamentals of Data Mining
Data Mining and Bioinformatics
Current and Future Trends

Bibliografia Obrigatória

Aggarwal, C. ; Data Mining: The Textbook, Springer, 2015
Dua, S. e Chowriappa, P. ; Data Mining for Bioinformatics, CRC Press, 2012
Finlay, S.; Predictive Analytics, Data Mining and Big Data (Business in the Digital Economy), Palgrave Macmillan, 2014
Han, J., Kamber, M. ; Data Mining – Concepts and Techniques, Morgan Kaufmann, 2011
Kudyba, S. ; Big Data, Mining, and Analytics: Components of Strategic Decision Making, Taylor & Francis Group, LLC , 2014
Larose, D.; Data Mining and Predictive Analytics, Wiley, 2015
Provost, F. e Fawcett, T. ; Data Science for Business: What you need to know about data mining and data-analytic thinking, O'Reilly Media, 2013
Sharda, R.; Delen, D.and Turban, E. ; Business Intelligence, Analytics, and Data Science: A Managerial Perspective, Pearson, 2016
Sherman, R.; Business Intelligence Guidebook: From Data Integration to Analytics, Morgan Kaufmann, 2014
Shmueli, G.; Bruce, P.; Gedeck,P. and Patel, N. ; Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, Wiley, 2019

Métodos de ensino e atividades de aprendizagem

Teaching Methodologies
- Expositive and participative with the purpose of promoting learning by discovery, through individual and group exploration of the importance of Business Intelligence and their support infrastructure; concepts application and problem solving, that allow the achievement of the main concepts of Business Intelligence and the knowledge discovery in databases through the application of Data Mining techniques.

In order to foster the development of group skills, a practical group work will be carried out with discussion in the specific learning area will take place.

Software

Microsoft PowerBI
Anaconda

Tipo de avaliação

Distributed evaluation with final exam

Componentes de Avaliação

Designation	Peso (%)
Teste	60,00
Trabalho escrito	40,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	30,00
Estudo autónomo	30,00
Frequência das aulas	60,00
Total:	120,00

Obtenção de frequência

Continuous assessment comprises the preparation, presentation and discussion of two group works (TG) - 40% and the completion of two individual theoretical tests - 60%.
Conditioned to a minimum score of 08 points.

Final evaluation periods:
The final assessment comprises the completion of an individual theoretical/practical examination.

Fórmula de cálculo da classificação final

Continuous assessment comprises the preparation, presentation and discussion of two group works (TG) - 40% and the completion of two individual theoretical tests - 60%.

Final classification = 30% Test 1 + 30% Test 2 + 20% Work 1 + 20% Work 2

Avaliação especial (TE, DA, ...)

The final evaluation includes an individual theoretical/practical examination.

Melhoria de classificação

The final evaluation includes an individual theoretical/practical examination.