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Data Analysis

Code:

MEC111014

Sigla:

AD

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2022/2023 - 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
MEEC	10	Plano de Estudos_2020	1	-	7,5	75	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

B-learning

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.
Classes will follow a calendar initially presented to students containing in-person and remote classes and deliverable tasks/milestones. Remote classes explore the use of technologies involved in a digital environment, this and its methods, in selected subjects, in a more pedagogically productive way. The face-to-face classes maintain exposition, exploration of examples/case studies and exercises to introduce and frame the subjects. The number of classes in the remote modality is less than 50% of the total classes.

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)