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Data Analytics

Code:

MES2

Sigla:

AD

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2021/2022 - 1S

Ativa?	Yes
Unidade Responsável:	Departamento de Sistemas e Informática
Curso/CE Responsável:

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
MES	18	Plano de Estudos 2017-2018	1	-	7,5	-	202,5

Língua de trabalho

Portuguese

Objetivos

At the end of this course the student should be able to:
• Identify the challenges and requirements for persisting and analyse big data
• To know the development phases of BI/Analytics projects
• Modelling and specifying a Datawharehouse
• Understand the best practices for coding the ETL process
• Conduct analytical queries to a multidimensional Datawharehouse through SQL OLAP extensions
• Evaluate, select and configure OLAP systems
• Differentiate and select several datamining algorithm classes, and their usage assumptions
• Develop visualizations for data and respective analyses results reporting, over existing platforms

Resultados de aprendizagem e competências

• Know how to guide a BI project


• Establish data models


• Properly select and apply algorithms for the problem

Modo de trabalho

Presencial

Programa

1. OLTP, OLAP and big data
2. Frameworks for BI project development
3. Dimensional data Modelling
4. Physical design of Datawharehouses
• New paradigms for distributed data persistence and processing
5. The ETL process
• Phases and Technologies
6. Analytical Query
• SQL OLAP extensions
• OLAP systems vs Emergent technologies and tools
7. Datamining
• Common applications
• Distinctions among supervised methods vs non-supervised
• Models evaluation
• Tools
8. Reporting and Visualization
• Technologies for development and configuring interactive visualization elements
9. BI and NoSQL sources
• Alternative data representations
• The complementarity and integration of hybrid systems
10. Module II: An Application Case: Web analytics

Bibliografia Obrigatória

Ralph Kimball e Margy Ross; The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Wiley, 2002
Foster Provost, Tom Fawcett; Data Science for Business: What you need to know about data mining and data-analytic thinking (, O'Reilly Media, 2013. ISBN: 1449361323

Métodos de ensino e atividades de aprendizagem

Theoretical presentation of concepts. Analysis of case studies. Practical application of concepts

Tipo de avaliação

Distributed evaluation without final exam

Componentes de Avaliação

Designation	Peso (%)
Trabalho escrito	50,00
Exame	50,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	20,00
Trabalho escrito	10,00
Trabalho laboratorial	32,00
Estudo autónomo	80,00
Frequência das aulas	60,00
Total:	202,00

Obtenção de frequência

Attendance at labs, seminar and project evaluation presentations

Fórmula de cálculo da classificação final

Module I (40%) = 20% Labs (min9)+ 30% Seminar (min9) + 50% test (min9)
Module II (60%) = 30% Labs (min9)+ 30% projet  (min9)+ 40% test (min9)

Exame: 50% Projet (min9) + 50% Exam (min9)