Competencies and objectives

 

Course context for academic year 2025-26

This course offers a dual introduction to classical machine learning methods and text data analysis, both with a focus on economic applications. The first part of the course covers supervised and unsupervised learning techniques, including regularization methods for regression and classification, decision trees, ensemble learning, clustering, and dimensionality reduction, among others. Emphasis is placed on model evaluation, validation strategies, and the practical implementation of these algorithms to answer empirical questions in economics. Students also learn to distinguish between predictive and causal modeling approaches, and how descriptive, predictive, and prescriptive methods can serve different analytical goals.
The second part of the course introduces core techniques in text mining and natural language processing. It covers the acquisition, cleaning, and structuring of text data, including multilingual and heterogeneous sources, and the application of machine learning methods to tasks such as classification, topic modeling, and sentiment analysis. Applications focus on leveraging textual information to extract economic insights and inform decision-making. Python is used as the main programming environment supporting practical implementation.

 

 

Course competencies (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2025-26

Skills/Skills

  • RA05 : Understand how technical problems in research articles have been addressed and be able to replicate the empirical analysis and simulation experiments on which they are based
  • RA12 : Use data visualization, data mining, and text mining techniques.
  • RA13 : Know how to apply data science techniques to real economic problems, both for research and decision-making

 

Skills/Competences

  • RA02 : Deeply understand and apply descriptive, causal, and predictive analysis methods applied to economics
  • RA04 : Formulate relevant economic problems precisely and provide appropriate solutions using theoretical, empirical, or simulation-based analysis
  • 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
  • RA15 : Know and handle technologies, algorithms, and machine learning tools (both supervised and unsupervised)

 

 

 

Learning outcomes (Training objectives)

  • Gain deep knowledge of machine learning algorithms and tools (including supervised and unsupervised learning).
  • Design and use techniques for validation and comparison of machine learning algorithms.
  • Apply descriptive, predictive, and prescriptive analysis methods, clearly distinguishing between predictive and causal modeling cultures.

 

 

Specific objectives stated by the academic staff for academic year 2025-26

  • Have an in-depth knowledge of and work with machine learning technologies, algorithms, and tools (including supervised and unsupervised learning).
  • Design and use metrics and techniques for the validation and comparison of machine learning algorithms.
  • Know in depth and apply descriptive, predictive and prescriptive analysis methods, properly distinguishing the two cultures of statistical modelling (prediction versus causality)
  • Know the main natural language processing techniques for automatic text analysis and their usefulness in text mining tasks.
  • To know the specific aspects of the application of supervised and unsupervised techniques to automatic text analysis.
  • Know how to apply different models of information retrieval, document categorisation and clustering, information extraction, opinion and sentiment mining, multilingual text mining and other possible applications of text mining in economics to specific texts.

 

 

General

Code: 49167
Lecturer responsible:
Martínez Martín, Ester
Credits ECTS: 3,00
Theoretical credits: 0,20
Practical credits: 1,00
Distance-base hours: 1,80

Departments involved

  • Dept: Software and Computing Systems
    Area: LANGUAGES AND COMPUTING SYSTEMS
    Theoretical credits: 0,08
    Practical credits: 0,32
  • Dept: Computer Science and Artificial Intelligence
    Area: CIENCIA DE LA COMPUTACIO, INTEL·LIGENCIA ARTIFICIA
    Theoretical credits: 0,12
    Practical credits: 0,68
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught