Studying virtually at Complutense University of Madrid

ATTENTION: All of the modules offered for the Una Module Exchange at Universidad Complutense de Madrid are taught in Spanish. If you do not speak sufficient Spanish (B1/B2 level), please do not apply for these modules. Please note that the number of participants is limited to 3 students for both modules.

Big Data Management (6 ECTS)

Term offered: Autumn Term 2026

Teaching: in Spanish, Synchronous – students must attend live sessions at scheduled times

This module introduces the foundations of Big Data, including NoSQL databases, distributed computing and cloud-based data architectures. Students learn how to preprocess data, apply core machine learning methods and work with large-scale datasets. By the end of the course, they are able to manage and analyse big data using modern tools and techniques.

Detailed module description on UCM homepage

Applied Statistics and Data Mining (9 ECTS)

Term offered: Autumn Term 2026

Teaching: in Spanish, Synchronous – students must attend live sessions at scheduled times

This module provides a comprehensive introduction to statistical data analysis and predictive modelling. Students work with methods such as time series analysis, clustering, classification and neural networks, combining theory with hands-on applications in SAS, R and Python. By the end of the course, they are able to apply and interpret supervised and unsupervised models in real-world contexts.

Detailed module description on UCM homepage

Neural Networks and Parallel Computing (3 ECTS)

Term offered: Spring Term 2027

Teaching: in Spanish, Synchronous – students must attend live sessions at scheduled times

This module introduces parallel computing techniques and their application to numerical methods, alongside the fundamentals of neural networks and deep learning. Students explore different learning methods and implement practical examples in computer lab sessions. By the end of the course, they understand how parallel computing and neural networks can be applied to real-world problems.

Detailed module description on UCM homepage

Prediction Techniques (3 ECTS)

Term offered: Spring Term 2027

Teaching: in Spanish, Synchronous – students must attend live sessions at scheduled times

This module focuses on methods for analysing and forecasting time series data. Students learn techniques such as ARCH and GARCH models, multivariate models and cointegration, with an emphasis on real-world applications. By the end of the course, they are able to model and predict time-dependent data and interpret forecasting results.

Detailed module description on UCM homepage

Detailed academic calendar 

please note that the dates may vary dependent on the programme. Consult the contact person for more details.  

Application Guide

1. Fill out the following form, providing all necessary information, as well as uploading your current transcript of records. Application Form
2. To access the form you haev to use a Gmail account. You can later indicate another email address to be contacted.
3. Once we have reviewed the application we will contact students, to enrol them in the module(s) they wish. 

Required Documents

1. A copy of your Curriculum Vitae.
2. Your Bachelor's and Master's Transcript of Records.
3. A certificate of your Spanish language proficiency.

Participation Limitation

In order to provide online participants with the same learning quality, as students who participate on-site, there are limited spots reserved for DSAI Module Exchange participants. A maximum of 3 students will be accepted per module. Once the selection has been made, you will be contacted.