Business Inteligence

Recommended Prerequisites

Basic knowledge of Microsoft Excel. 

Teaching Methods

The methodology includes lectures, audiovisual materials, cases, and classroom discussion. It seeks to promote active and participatory learning to understand the contents addressed and their application to developing innovative solutions that are socially relevant, responsive to people's expectations, and better outcomes.

Learning Outcomes

Provide students with essential knowledge and skills in Information Systems, Data Governance and Data Science, enabling them to act as “innovation promoters” in the various sectors of economic activity and understand their main challenges.
Improve skills, develop and update specific knowledge about Business Intelligence, and use specific tools for this purpose.
Use Excel as a powerful and flexible tool in Business Intelligence solutions combined with Power BI for extensive data visualization and analysis capabilities.
Develop the ability to collect, organize and critically analyze content related to Business Intelligence in various economic activity sectors improving decision making and communicating value creation proposals.

Work Placement(s)

No

Syllabus

Information Systems and Data Governance: concepts and classification; Information Management and Data Governance; Data
Management, Data Analytics and Business Intelligenc (BI); Data Modelling.
Data Science in a Big Data word: definitions; facets of data; the data science process; based data processes (BI, Self-service-BI, Business Analytics, Data Science); Data Visualization.
Data management, analysis and visualization with Excel: introduction; importing and cleaning up data; tables (introduction, formatting and pivot tables); basic statistical functions; using formulas for conditional analysis; working with data/time; conditional formatting; data validation; graphics (traditional and pivot charts, sparklines and segmentation); matching and lookups.
Combining Excel and Power BI: an introduction to collect, model, analyze, explore and visualize data using the functionality of both applications

Head Lecturer(s)

Víctor Manuel dos Reis Raposo

Assessment Methods

Assessment
Other: 10.0%
Mini Tests: 30.0%
Project: 60.0%

Bibliography

Alexander, M., Kusleika, R., & Walkenbach, J. (2018). Excel 2019 Bible. John Wiley & Sons.
Camões, J. (2016). Data at work: Best practices for creating effective charts and information graphics in Microsoft Excel. New Riders.
Cielen, D., & Meysman, A. (2016). Introducing data science: big data, machine learning, and more, using Python tools. Simon and
Schuster.
Knight, D., Pearson, M., Schacht, B., & Ostrowsky, E. (2020). Microsoft Power BI Quick Start Guide: Bring your data to life
through data modeling, visualization, digital storytelling, and more. Packt Publishing Ltd.
Kusleika, D. (2021). Data visualization with Excel dashboards and reports.
Turban, E., Pollard, C., & Wood, G. (2020). Information Technology for Management: On-demand Strategies for Performance,
Growth and Sustainability: John Wiley & Sons.
Wexler, S. (2021). The big picture: How to use data visualization to make better decisions - faster.