The ability to develop decision support systems is highly valued in the job market because it helps organizations make more informed and effective decisions, optimizing processes and resources, reducing risks, and creating competitive advantages. This makes professionals with these skills essential in an increasingly data-driven world. In this course, we cover the fundamentals and explore the capabilities of Python for acquiring, organizing, manipulating, visualizing, and analyzing data. The emphasis is on acquiring and integrating data from various sources, preparing data for analysis, creating informative visualizations, and conducting statistical analyses to describe situations, investigate associations, identify patterns, and develop predictive models.
Semestral
S1. Overview of Decision Support Systems, Business Intelligence, and Analytics
Business Intelligence
Indicators
Analytics
Data Warehousing
Online Analytical Processing
S2. Knowledge Discovery in Databases
Phases of the process
CRISP-DM Methodology
S3. Information Visualization
ETL (Extract, Transform and Load) Tools
Business Reporting, Visual Analytics, and Dashboards
S4. Statistical Data Analysis
Descriptive Statistics
Inferential Statistics
S5. Machine Learning in Decision Making
Machine Learning and its Use in Decision Support Systems
Supervised and Unsupervised Learning
Machine Learning Applications in Decision Making: Clustering, Regression, and Classification
S6. Python for Decision Support Systems
LO1. Understand the concepts of Business Intelligence, Analytics, and Decision Support.
LO2. Be able to analyze and map business processes in dimensional modeling.
LO3. Understand the architecture for implementing Data Warehousing solutions.
LO4. Understand the phases of the knowledge discovery process in databases.
LO5. Develop ways of information visualization, including dashboards.
LO6. Distinguish and apply data analysis techniques for exploratory, explanatory, or predictive purposes.
LO7. Address business questions through the application of Descriptive and Inferential Statistics techniques.
LO8. Apply Machine Learning techniques and explore how to use them in Decision Support Systems.
LO9. Distinguish situations where clustering, regression, and classification techniques are applicable.
LO10. Utilize Python for data acquisition, preparation, visualization, and analysis.
Mandatory
The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises, with and without the use of a computer.
The assessment is made by continuous assessment or written exam. The continuous assessment consists of one group work.
Português
Não
Harrison, M. (2016) Pandas Library: Python Tools for Data Munging, Data Analysis, and Visualization. Treading on Python Series.
Hastie, T.; Tibshirani, R.; Friedman, J. (2009). The Elements of Statistical Learning Data Mining, Inference, and Prediction, New York: Springer
McKinney, W. (2018) Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Python. USA: O’Reilly.
Santos, M. Y., Ramos I. (2017). Business Intelligence – Da Informação ao Conhecimento (3.ª ed.). Lisboa: FCA Editora.
