Data Science

The course provides a theoretical and practical introduction to a number of topics in data analysis and statistical learning, with special emphasis on applications in the field of economics.

These topics may include:

• Linear, non-linear and logistic regression
• Cross-validation
• Bootstrapping
• Decision trees and boosting
• Support vector machines
• Clustering
• Neural networks
• k-nearest neighbors (k-NN)
• Data visualization
• Generative AI
• Explainable AI

The course provides an introduction to the use of the programming language R or Python for data analysis. Use of other computer tools, such as SQL, can also be included.

Knowledge

The candidate should:

• Have a good knowledge of the basic techniques of data science
• Be able to link applications of data science to issues related to the economic-administrative field

Skills

The candidate should:

• Be able to perform basic data analyses in the programming language R or Python
• Be able to understand and evaluate advanced data analyses as well as results from certain machine learning techniques

General Competence

The candidate should:

• Be able to use data science to express, analyze and communicate economic issues
• Have an understanding of data science and basic machine learning that can form the basis for further studies and lifelong learning

Lectures and exercises.