Master's Degree in Big Data Analysis in Economics and Business
Academic year 2022-23
You may also check this information for the 2021-22 academic year.
- New student profile and admission criteria
- Academic and professional goals
- Access to other study programmes and career opportunities
- Structure of the study programme
- Final Exam
- Evaluation criteria and exams
- Study programme leadership
- Composition of the Academic Committee
- Credit Recognition and Transfer Committee for MADM
The master is addressed to engineers (all types), graduates in Physics, Mathematics, Statistics, Economics, Business Administration and Management (ADE) (it is recommended to the students of the UIB’s ADE degree to have completed the optional subject (20632) Survey Analysis and Multivariate Techniques).
Specific access tests to the Master degree in "Analysis of Massive Data in Business and Economics" are not defined. However, it is established as access criteria for new students a Bachelor degree belonging to one of the following branches of knowledge: engineering and architecture, Social Sciences and Law (Economics and Business Administration and Management) and Science (Statistics, Mathematics, Physics). The recommended profile is a student with a Bachelor Degree in Computer Engineering, Mathematics, Statistics, Physics, Economics or Business Administration. For other degrees of the knowledge engineering and architecture branch, the Degree’s Academic Committee (CAT) will assess whether the student has received the necessary training and acquired the skills needed to pursue the Master without carrying out supplemental courses, which will affect the acceptance or not of the student in the program. The admission criteria will be weighted, taking firstly into account the degree of access to the Master and secondly the average grade of the student’s academic record in the studies of access to the Master.
Applications for admission to the master will be resolved by the CAT, named by the competent organ, and chaired by the Director of the Master, who will conduct proceedings in accordance with applicable regulations. The Commission of Studies will meet before the end of the fifteen days following the pre-registration deadline to assess applications according to the requirements (Royal Decree 1393/2207) and the admission criteria outlined above. If the number of interested students exceeds the numerus clausus, transcripts will be weighted following the method used in the selection phase to award grants for training university teachers (FPU) for the current year or the previous year if the call has not been published. The Degree’s Academic Committee will establish an access list, ordered by merits that will be followed in the registration process.
Admission of students once the academic year has started will be considered individually by the organ responsible for the Master will comply with the regulations of the university. The decision on admission will be conditioned by at least the following criteria:
- Places available in the program.
- Students cannot be admitted to courses that have already been completed, unless recognition of previous studies compensates for this course.
In the case of students with special needs or disabilities, their admittance into the program will be carried through in accordance with the twenty-fourth additional provision of Organic Law 4/2007 of April 12 on the inclusion of people with disabilities in universities. Positive action measures to ensure access of these students to the Master will be contemplated, provided they meet the conditions in the regulations in force.
The proposal of this Master's degree has its origin in the growing need to provide training in management technologies and analysis of massive data, and their applications in response to the scientific, economic and business challenges in the area known internationally as "Big Data" or "Big Data Analytics". This is an area with a short trajectory but with a huge potential, able to generate products and services with high added value for the society. The 90% of the existing data at the global level have been generated in the past two years and approximately 90% of them are not structured in nature (Kim, G. , Ikimi, S. , Chung, J. "Big Data Applications in the Government sector", Communications of the ACM, 57, 3, 2014). From an information technology point of view, this huge volume of data introduces challenges at different levels. In the first place, related to new models able to manage these data in an efficient manner so as to facilitate the processing, storage, and access to massive amounts of information (available in a variety of formats and with different levels of structuring). Secondly, related to the ability to explore, organize and analyze these data to extract knowledge and make predictions. In third place, related to the identification of new business areas that, assisted by this type of technology used in decision-making, can give rise to innovative products and services that improve the competitiveness of enterprises and public institutions.
From an academic point of view, these challenges require a comprehensive training in a wide range of aspects as diverse as Information Technology, Data Mining, Statistical Learning, Decision Making, Economics, Business and Business Intelligence. At present, there is a high demand for professionals with skills in this area. Depending on the intensity of its specialization in the three key areas (Information Technology for the management of massive data, tools in Management and Analysis of massive data or applications) there are different types of new professionals and scientists. Big Data (or "Data Developers") programmers, data analysts or scientists ("Data Analysers" or "Data Scientists"), and corporate experts in data ("Data Businessmen") are some of the new professions that have arisen around Big Data (Harris, H. , Murphy, S. , Vaisman, M. "Analyzing the analyzers. An introspective Survey of Data scientists and their work." O'Reilly, 2013). This new curriculum covers transversely these large areas preparing the graduates' for their future activity, both professionally in companies of various sectors and scientifically as researchers in centers or I+D+i teams, private or public, and/or for completing a doctoral thesis in any of the highlighted areas.
The fundamental purpose of the degree is to prepare professionals with a transversal and very versatile training, covering a broad spectrum of knowledge and easily adaptable to significantly different environments. The development of the use of information and communications technologies (TIN) among citizens and businesses has as a consequence the enrichment of a country, as it translates into a potential increase in their productivity. Finally, the issues associated with quality, both from a quantitative approach to the statistical tools and qualitative approach to management tools, are conclusive arguments of companies and professionals.
The Master's degree aims for specialization that allows access to a new level of knowledge, and that can also act as a continuing education program for professionals who need to develop the competencies offered in the new curriculum. The interest in management technologies and massive data analysis is common to many branches of knowledge, in particular, to the branches related with Social Sciences.
The Master offers knowledge and skills that will help the students analyze, decide, implement, and optimize technology-based initiatives related to the analysis of massive data, which will be of broad utility to improve not only their skills but also their professional expectations.
Finally, since the master is also addressed to future researchers, students with this profile may choose to continue their training in several of the doctoral programs offered at the UIB. In particular, we can mention the following: 1) PhD in Applied Economics, with quality certificate from the Ministry of Education, with lines of research in econometrics and in tourism economy; (2) Doctoral Degree in Information and Communication Technologies; 3) Doctoral Degree in economics, organization and management, along with the Universidad Pública de Navarra and the Universidad Autónoma de Barcelona.
The Master's Deggree in "Analysis of Massive Data in Business and Economics" is a Master of 90 credits.
To obtain the Master's degree, with professional orientation or focused in research, the student must take:
- 33 credits of required courses.
- 18 optional credits depending on specialization.
- 9 optional credits chosen from among specialization credits not coursed above or credits from the rest of specialties.
- 12 credits of external practices.
- 18 credits corresponding to the mMster’s Final Project.
The external practices credits can be recognized for accreditation of work and professional experience, as long as the student justifies and demonstrates that the work and professional experience is directly related to the subject and the competences of the Master.
More information about the specializations and subjects of the Master, and other optional courses not linked with the specialties is available on the website - section "Subjects" of this master's degree.
Massive Data Analysis Technologies
Statistical Learning and Decision Making
Social and Economic Networks
Massive Data Econometrics
Massive Data in Enterprise Management
Depending on the itinerary chosen by the student, the master provides the following specializations: "Computer Technologies for Massive Data Management", "Tools for Management and Intelligent Data Analysis" and "Techniques and Applications in Economic and Corporate Management".
The list of subjects that compose each of the specializations follows below.
Computing Technologies for Massive Data Management
Semantic information technologies
Massive Data Management and Storage
Data and Text Mining
Tools for Management and Intelligent Data Analysis
New trends in data mining
Statistical Techniques for Imprecise Data
Optimization Techniques for Imprecise Data
Statistical learning and decision making II
Simulation and Sampling Tools for Massive Data
Techniques and applications for economic and corporate management
Time Series Analysis
Decision Making and Game Theory
Finance and Econometrics with High Frequency Data
Data Mining Applications in Tourism
Human Resource Management
Management of Healthcare Organizations
Text Mining for Social Sciences
In order to obtain the master’s degree a final comprehensive exam is not considered.
Following the recommendations of the European Higher Education Area, all subjects of this Master's Degree will be evaluated following a continuous assessment process. At the beginning of the course, the assessment criteria of each subject will be available to the students in the Master's Degree website.
Dr. Antonio Vaello Sebastiá
Dr. Óscar Valero Sierra
Profesor Coordinador del TFM
Dr. Isaac Lera Castro
Profesor Coordinador de las Práctiques Externas
Dr. Antonio Bibiloni Coll
- Antonio Vaello Sebastiá
- Antonio Bibiloni Coll
- Tomás Del Barrio Castro