6 ECTS; 2º Ano, 2º Semestre, 28,0 PL + 28,0 TP + 5,0 OT , Cód. 814346.
Lecturer
- Vasco Renato Marques Gestosa da Silva (1)(2)
(1) Docente Responsável
(2) Docente que lecciona
Prerequisites
Not applicable
Objectives
1) Understand the fundamental principles of analytics-oriented data modeling; 2) Design and implement efficient data models; 3) Apply best practices in dimensional modeling (facts, dimensions, and hierarchies); 4) Use the DAX language to create measures, calculated columns, and KPIs; 5) Develop effective, interactive, and user-oriented visualizations; 6) Create analytical reports and dashboards to support decision-making; 7) Organizational apps
Program
A. Introduction to Data Modeling and Visualization
Role of modeling in the Business Intelligence cycle
Difference between transactional and analytical databases
Overview of the Power BI ecosystem
B. Data Acquisition, Preparation, and Transformation
Data sources (files, databases, online services)
ETL vs. ELT
Power Query: data cleaning, transformation, and enrichment
Standardization and handling of inconsistent data
C. Data Modeling
Fundamental modeling concepts
Dimensional modeling:
o Fact tables
o Dimension tables
o Granularity
Star and snowflake schemas
D. DAX Language (Data Analysis Expressions)
Basic concepts: row context and filter context
Measures vs. calculated columns
Most used DAX functions (SUMX, CALCULATE, FILTER, etc.) E. Data Visualization
Types of charts and when to use them
Interactivity: filters, segmentations, and drill-down
F. Publishing and Sharing Reports and Dashboards
Publishing to Power BI Service
Workspaces, permissions, and sharing
G. Microsoft Power Apps
Evaluation Methodology
The course attendance assessment consists of completing assessment exercises (50%) and a final assessment project (50%). The final grade for the course results from the weighted average of the various assessment components. The student obtains approval in the course and is exempt from the exam, in accordance with the provisions of Points 11 and 12 of Article 11 of the IPT Academic Regulations.
The exam/resit exam assessment consists of completing a practical project (100%). The student obtains approval in the course, in accordance with the provisions of Points 11 and 12 of Article 11 of the IPT Academic Regulations.
Bibliography
- Côrte-Real, N. (2022). Big Data & Analytics. (Vol. 1). Lisboa: Influência
- Gama, S. e Gonçalves, D. e Santos, B. e Moreira, J. (2025). Visualização de Informação Principíos e Técnicas para Compreensão e Comunicação de Dados. (Vol. 1). Lisboa: FCA
- Sordi, J. (2019). Modelagem de Dados. (Vol. 1). Brasil: érica
Teaching Method
Expository/Demonstrative and practical exercises.
Software used in class
Microsoft Power BI; Microsoft Power Apps; Microsoft Teams
