Base Knowledge
Information Systems
Relational Databases (mysql,postgres e SQL Server)
Programming Languages (python,c, c++, php, asp, or java)
Teaching Methodologies
The theoretical classes will be aimed at presenting content and complemented with practical problems to promote and stimulate collective discussion.
In the practical classes, some exercises will be conducted focusing on the implementation of small projects using development tools such as SQL Server, Visual Studio, Excel, and PowerBI.
Learning Results
Understanding the techniques and implementation models of warehousing, including the design and implementation of applications for data extraction, transformation, and integration.
Development and implementation of applications for multidimensional database systems.
Having knowledge in specific aspects of Information Systems (IS).
Being proficient in using various technologies employed in IS within the field of Business Intelligence.
Program
SCM and CRM.
Information Systems Development Methodologies.
Data Warehousing Systems: Introduction to data warehousing; the data warehouse as an organization’s information infrastructure; environment and functional structure of a data warehouse; life cycle and incremental development of a data warehouse. Design, implementation, and administration of a data warehouse, including data extraction, transportation, transformation, and integration systems.
Modeling Tools.
Online Analytical Processing (OLAP) Systems: Fundamentals of analytical data processing; data structures for analytical data processing; optimization and dynamic restructuring of cubes; materialization of views: centralized and distributed perspectives. Administration of multidimensional database management systems.
Building ETL Pipelines with Python.
Development of multidimensional database system projects; definition of data access criteria. Use of analytical processing systems.
Development of information system projects using Visual Studio + SQL Server + Excel + Power BI Desktop + Python.
Curricular Unit Teachers
Filipe Alexandre Almeida Ningre de SáInternship(s)
NAO
Bibliography
Attobrah, M. (2024). ETL Pipeline. In Essential Data Analytics, Data Science, and AI: A Practical Guide for a Data-Driven World (pp. 27-46). Berkeley, CA: Apress.
Pandey, B. K., & Schoof, E. R. (2023). Building ETL pipelines with Python. Packt Publishing.
Ramos, I., & Santos, M. Y. (2017). Business Intelligence: Da Informação ao Conhecimento (3ª ed.). FCA.
Kimball, R., & Ross, M. (2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (2nd ed.). Wiley Computer Publishing.
Inmon, W. H. (2005). Building the Data Warehouse (4th ed.).
Magalhães, A. (2017). Business Intelligence no SQL Server. FC.
Laudon, K., Laudon, J., (2019), Management Information Systems (12ª ed.), Pearson. Cota 1A-13-64
Han, J., Kamber, M., (2006), Data Mining: Concepts and Techniques, Morgan Kaufman. Cota 1A-19-11, 1A-19-20
Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming and Delivering Data. Wiley Technology Publishing.
Allington, M. , (2018), Supercharge PowerBI, (1st ed), Kindle