Extraction, Transformation, Loading and Visualization

Classes will be provided with ETL tools and data visualization tools, taking advantage of the education licenses available at the School
corresponding to Power BI, Qlik and Tableau, so that students become familiar with them and develop skills in their handling.
Considering the difficulty in working with real data from companies, the use of data available in open data repositories such as data.gov.pt
or those from the Public Administration Transparency Portal, transparency.gov.pt, or data from international governments such as data.gov,
from the United States of America.
Classes will be theoretical-practical with exposure of theoretical content, followed by the application component in the context of preparation
for data analysis, visualization and decision making.
Part of the work will be carried out in class, in order to allow the necessary follow-up, and there will also be hours of autonomous work for
students.
The works will be presented in class and on a peer-review basis, that is, each group will have to present their work and answer the
questions in addition to analyzing the work of another group, proposing suggestions for improvement and addressing constructive criticism.
Students will be guided in order to fulfill the defined work plan, with delivery of small tasks throughout the semester, which will be finalized
with a pitch for each of the final versions of the 2 works presented.
Students will also be invited to attend out-of-class events provided by partner entities and a meet-up will also be held with one of the
School’s partner companies, to be held at the School. This event will represent one of the services provided to society by this Curricular
Unit, and students will have the opportunity to be actively involved in the preparation of the Meet up and, during the event, to see new
features provided for the tools in use, use cases in companies and establish a connection with the Course Units of the last semester of the
Degree, in addition to promoting and expanding their network of business knowledge, enhancing their opportunities to connect to the job
market.
Two optional challenges will also be launched:
1) participation in a national/international competition on the subject of this UC
2) conference submission with Scopus indexing: students will be invited to create a version, in a scientific article model, that includes the 2
works developed with the aim of being published in a conference with Scopus indexing, in order to promote Research activities together of
these undergraduate students.

This subject aims to achieve the following learning objectives:
– Know the Real-Life ETL cycle (Extraction, Transformation, Loading)
– Master the different stages of the ETL process (establishing the connection, in the data transformation component, to the knowledge
acquired in the Data Analysis CU)
– Know, compare and use ETL tools
– Know and apply data visualization rules appropriate to the context of data users, promoting better understanding and decision making
based on the data.
– Knowing how to use the most appropriate techniques for the data under analysis so that, starting from raw data, it is possible to obtain the
most effective visualizations, going through the ETL process, until the visualization is completed.

1 – Data Concepts, Data-driven Culture and ETL
1.1 – Data Analysis Cycle
1.2 – Location and types of Data Sources: Data Extraction
1.3 – Data Preparation and Transformation Processes
1.4 – Data warehouses vs Data Lakes: Data loading
1.5 – Data Governance Principles and associated professional profiles
1.6 – Data Curation
1.7 – Ethical Aspects of Data Use and General Data Protection Regulation
1.8 – Open Repositories
1.9 – Tools for ETL and comparative study
2 – Data Visualization
2.1 – Rules for preparing data visualization
2.2 – Tools for data visualization: identification and comparative study
2.3 – Planning, monitoring and discussion of the data visualization process
3 – ETL design and data visualization

NAO

Aspin, A. (2022). Pro Data Mashup for Power BI: Powering Up with Power Query and the M Language to Find, Load, and Transform Data,
Apress Editions
Deckler, G. (2022). Learn Power BI: A comprehensive, step-by-step guide for beginners to learn real-world business intelligence, 2nd
Edition, Packt Publishing
Deckler, G., Powell, B. (2022). Mastering Microsoft Power BI: Expert techniques to create interactive insights for effective data analytics and
business intelligence, 2nd Edition, Packt Publishing
Kimbal, R., (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data
Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.
Few, S. (2019). The Data Loom: Weaving Understanding by Thinking Critically and Scientifically with Data, Analytics Press.
Few, S. (2019). Now You See It: Simple Visualization Techniques for Quantiative Analysis, Analytic