Competencies and objectives

 

Course context for academic year 2026-27

This course is designed to deepen students' programming knowledge. The course focuses on programming in the Python language, aimed at solving problems in the fields of economics and data science. The main elements of the language will be studied, along with how to use them to efficiently and robustly handle large volumes of data. In addition to the core elements of the language, the course will also cover major scientific computing libraries such as NumPy By the end of the course, students should be able to effectively solve economics and data science problems using the Python environment.

 

 

Learning outcomes / Course competencies (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2026-27

Skills/Competences

  • RA09 : Understand and master the fundamental concepts of logic, algorithms, and computational complexity, and their application to problem-solving
  • RA14 : Be able to develop and learn autonomously about topics related to Economics and Data Science

 

 

 

Learning outcomes (Training objectives)

  • Be able to analyze problems solvable by a computer and design algorithms to solve them.
  • Implement algorithms using structured programming techniques.
  • Understand and use a high-level programming language.

 

 

Specific objectives stated by the academic staff for academic year 2026-27

  • Set up the Python development environment using tools that allow the installation of required libraries and the management of their dependencies.
  • Identify the most appropriate data structures and processing methods for input data, considering the characteristics of the Python language and the scientific libraries NumPy and SciPy.
  • Develop algorithms that efficiently process large volumes of data obtained from various sources (local or online files), organizing and interpreting them to generate datasets prepared for further processing.
  • Interpret real-world problems, analyze them, and identify the most suitable data structures and models for their porcessing.
  • Justify the decisions made during code development and explain the knowledge acquired.

 

 

General

Code: 49161
Lecturer responsible:
Araujo da Silva Costa, Angelo Gonçalo
Credits ECTS: 3,00
Theoretical credits: 0,00
Practical credits: 1,20
Distance-base hours: 1,80

Departments involved

  • Dept: Software and Computing Systems
    Area: Languages and Computing Systems
    Theoretical credits: 0
    Practical credits: 0,4
  • Dept: Computer Science and Artificial Intelligence
    Area: Science of the Computation, Artificial Intelligence
    Theoretical credits: 0
    Practical credits: 0,8
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught