Master's Degree in Big Data Analysis in Economics and Business
Degree data and benchmarks
New students: these students are starting their studies from the beginning for the first time. They may have recognised credits or not.
New SIIU students: these students are starting their studies from the beginning, registering on a programme for the first time and, in accordance with SIIU criteria, may have fewer than 10 credits (for a Master's) or 30 credits (for an undergraduate programme) recognised. This set of students may also be referred to as the optimum new entry group.
Registered students: these students have an active registration on a programme for an academic year. This set of students may also be referred to as the total student population.
- Registration reservations are not included (due to the students awaiting a place at another university or credit recognition)
- Registration cancellations are not included
- Students with unpaid debts are not included
Graduates: these students have passed (passed or accredited) all credits required for the degree programme and have, therefore, finished their course regardless of whether they have requested their degree certificate be issued or not.
Credits Taken and Passed
|Degree Success Rate||97%||97%|
Success rate: the percetage ratio between the number of passed credits and the number of credits taken for assessment.
Performance rate: the percentage ratio between the number of credits passed and the number of credits registered for.
Who directly oversees the quality of the degree programme?
- Tomás del Barrio Castro
- Isaac Lera Castro
- Antonio Vaello Sebastiá
The Quality Assurance Committee (CGQ) gathers all of the information regarding the degree programme (survey reports, data, statistics, complaints, suggestions, etc.) and analyses them. Here, you can see the regulations and duties of the Quality Assurance Committee (CGQ).
Planning improvement measures
Accountability and transparency
|Accreditation||Final accreditation report||08/08/2019|
|Monitoring||External follow-up report (2016-17)||24/09/2018|
|Monitoring||Annual follow-up and internal assessment report (2016-17)||23/03/2018|
|Verification||Final verification report||30/07/2015|
|Verification||Official university degree statement||10/07/2015|
End of master projects
- Inspector Sanitari basat en l’Anàlisi de Polaritats a Twitter
- Modelos ocultos de Markov para el etiquetado de texto
- Programación Lineal Borrosa: Una aplicación al Análisis de Revenue Management en una compañía Aérea
- Análisis y Predicción del Indicador de Presión Humana en les Illes Balears
- Causal inference and heterogeneous treatment effects
- Modelación y predicción del gasto de turistas en España enfocado desde el análisis de datos
- Modelado predictivo de la dirección de la cotización del Bitcoin utilizando índices de mercado, análisis de sentimientos en Twitter e índices de popularidad por término mediante Google Trends