Master's Degree in Data Analysis, Process Improvement and Decision Support Engineering

For both professionals and researchers, knowing how to model the problems that affect their work environment, knowing how to obtain and analyse data that faithfully describe the reality they are studying and having tools that allow them to control and improve the processes involved are an almost unavoidable necessity..

The aim of this master's degree is for both professionals and researchers to have the knowledge, skills and abilities to carry out these three tasks. This is achieved by establishing a solid training base in operations research, statistical methods, and quality techniques and models.

Many professionals in the fields of engineering and business administration, as well as researchers in these and related areas, show a strong lack of data analysis methods, process improvement and decision-making, topics that are only superficially addressed in the curricula of current undergraduate and postgraduate programmes. When these professionals join a company or a research team, they often find themselves lacking advanced tools to solve the problems that society demands of them. This master's degree aims to fill this gap by equipping students with the necessary knowledge to tackle the resolution of problems in their environment with guarantees.

Students will acquire the ability to apply the knowledge learnt and to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to process improvement and decision-making. This will enable them to analyse and solve complex problems in uncertain environments.

The master's degree is aimed at graduate students of both technical (engineering) and science (mathematics, statistics, biology, health sciences) degrees from Spanish and foreign universities, who are interested in the statistical treatment of experimental data, in the development of statistical and optimisation models, in the use of mathematical models and in the computer implementation of the corresponding algorithms.

The admission and selection procedure for candidates with any of the aforementioned qualifications will be based on the academic record, the suitability of the training profile for the master's degree training programme, the student's professional curriculum, complementary training and other accomplishments of interest, as well as an individual selection interview.

Department of Applied Statistics and Operations Research and Quality