Teaching Methodologies
The curricular unit combines theoretical foundations with applied practice, prioritizing theoretical–practical classes and active learning. The use of IDEs, computational libraries, and the critical support of AI tools promotes the connection between concepts and application, ensuring knowledge acquisition, skill development, and the consolidation of competencies in data analysis and problem solving.
Learning Results
At the end of the curricular unit, students should be able to distinguish different computational environments, use libraries and their respective methods, apply basic programming concepts, and develop simple programs to manipulate data and apply statistical techniques, critically using AI tools and computational libraries. The teaching method, based on theoretical-practical classes, ensures the acquisition of knowledge (data structures and statistical techniques), the development of skills (programming, exploring libraries, representing data, interpreting results), and competencies (autonomy in data analysis and interpretation, critical thinking, and integration of digital tools in engineering problems).
Program
Topic 1: Computational Environments and Data Operations
– Local environments and cloud environments.
– IDE functionalities as support for the programming process.
– Classes, libraries, and methods.
– Different data formats.
– File read and write operations for data manipulation.
Topic 2: Essential Programming Elements for Data Manipulation
– Basic programming concepts (variables and data types).
– Basic data structures (lists, vectors, matrices, dictionaries).
– Control structures (conditions and loops).
– Simple functions to organize and reuse code.
Topic 3: Statistical and Data Visualization Techniques
– Statistical variables (nominal, ordinal, interval, ratio scales).
– Data from univariate and multivariate samples.
– Graphical visualizations of datasets using computational libraries.
– Application of statistical techniques using computational libraries, with critical support from AI tools.
Curricular Unit Teachers
Sara dos Santos Escudeiro CruzInternship(s)
NAO
Bibliography
Devore, J. L. (2023). Probabilidade e estatística para engenharia e ciências (9.ª ed., trad. port.). Cengage Learning.
Haslwanter, T. (2022). An introduction to statistics with Python: With applications in the life sciences. Springer.
Lubanovic, B. (2025). Introducing Python (3rd ed.). O’Reilly Media.
McKinney, W. (2022). Python for data analysis (3rd ed.). O’Reilly Media.