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Data Mining

Code:

BINF022

Sigla:

DM

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2022/2023 - 2S

Ativa?	Yes
Unidade Responsável:	Departamento de 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	20	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

1. The challenges of Data Modeling and Analysis in Bioinformatics
2. Business Intelligence and Infrastructure
3. Fundamentals of Data Mining
4. Data Mining and Bioinformatics
5. Current and Future Trends

Bibliografia Obrigatória

Han, J., Kamber, M.; Data Mining – Concepts and Techniques, Morgan Kaufmann , 2011
He, Z.; Data Mining for Bioinformatics Applications, Elsevier Ltd., 2015
Kudyba, S.; Big Data, Mining, and Analytics: Components of Strategic Decision Making, Taylor & Francis Group, LLC, 2014
Larose, D. e Larose C.; Data Mining and Predictive Analytics, John Wiley & Sons, Inc, 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 Approach, Pearson, 2018
Shmueli,G.; Bruce,P.; Gedeck, P.; Patel, N.; Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, Wiley, 2020

Métodos de ensino e atividades de aprendizagem

Methodologies:

- Expository and Participatory in order to promote learning by discovery, through individual and group exploration about the Business Intelligence importance, and the supporting infrastructure, and concepts application and practical use cases resolution, supported by exercises that allow concretization the main concepts of Business Intelligence application and Knowledge Discovery in Databases through Data Mining techniques application.


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

If the individual knowledge assessment test is done in distance learning mode, and if the student obtains in this assessment test a classification equal to or higher than 17 points, the student must take an oral assessment to defend the obtained classification.

Software

Orange Data Mining
Microsoft PowerBI
Anaconda - Jupyter Notebook
R Studio

Tipo de avaliação

Distributed evaluation with final exam

Componentes de Avaliação

Designation	Peso (%)
Apresentação/discussão de um trabalho científico	20,00
Teste	50,00
Trabalho escrito	20,00
Participação presencial	10,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Trabalho escrito	34,00
Apresentação/discussão de um trabalho científico	1,00
Estudo autónomo	40,00
Frequência das aulas	60,00
Total:	135,00

Obtenção de frequência

To get approval in this unit the final grade must be equal to or greater than 10 points

Fórmula de cálculo da classificação final

Continuous assessment comprises:
- Preparation, presentation and discussion of a group work (TG) - 40%;
- two individual tests - 50% and
- activities during classes - 10%.

Avaliação especial (TE, DA, ...)

Individual final exam.

Final Grade = 100 % Exame

Melhoria de classificação

Improved classification includes an individual final exam.

Final Grade = 100 % Exame

Observações

In case of irregular situation detection (fraud) in the assessment process will be applied the Students Disciplinary Regulations of the Polytechnic Institute of Setúbal (Despacho No. 13714/2016, published in Diário da República, 2nd series, No. 219 on November 15th) taking also into consideration the Despacho No. 40/Presidente/2021 "Measures to be adopted in situations associated with fraud in the evaluation processes of the courses taught in IPS schools".