PresentationWe live in the era of dataification , in which thanks to the sensors and the traceability that we can extract from the so-called fingerprint from our trace on the internet or from the use of our digital devices, we are in a position to translate phenomena into data that are used to help monitoring and decision-making or that can even be transformed into something monetizable. But for this, it is a necessary condition to have techniques for its correct storage, processing and analysis, which is what should allow its enhancement, in a broad sense.
Due to its high volume, that is, due to its large dimension, the term Big Data has become popular to refer to this phenomenon, although it is not only the high volume, but also its high frequency and variety of forms (structured, unstructured, text , multimedia) the elements that characterize this era of Big Data, and that mark the path through which developments for massive data analysis have to run.
In this context, the new possibilities posed by the use of these information systems for the classification of clients, users and suppliers in order to carry out different forms of discrimination between them, or their role within the decision-making support system has caused the interest in extracting added value from data to become a priority for companies and institutions as key pillars of their competitive strategies.
In this order of things, having an adequate digital ecosystem that facilitates the data generation processes and the existence of channels that facilitate data transfers, requires, in addition to an adequate technological infrastructure, having the adequate human capital: that is, It must have specialists with advanced skills in the design, extraction, storage, treatment, analysis and interpretation that favor the progressive implementation of data-based decision making. Therefore, the availability of specialists in Big Data architecture and in Data Science is as important as having specialized companies that provide us with the necessary infrastructure to design and implement data-based management or to transform the data industry in a way value creation thanks to the availability of technology and talent to capture, store and analyze data.
To achieve a datadriven business culture, this ecosystem, this infrastructure must exist and that in turn will generate externalities that favor the interest of those in charge of carrying out strategic decision-making by designing and basing their governance on data-based decision systems . Therefore, having the talent of Big Data strategy makers is a crucial element to facilitate governance and the development of business intelligence strategies. An adequate supply of this type of talent, which is certainly scarce, is a sine qua non condition for this new data-driven culture to emerge and penetrate the entire business system. However, this talent is scarce, as evidenced by the high employability and salaries compared to other similar professionals. This situation is due, to a large extent, to the high demand for this type of specialists by large corporations anchored to old business schemes, which are aware that their digital transformation is key to being competitive. However, the implementation of this new culture is not easy, and it requires experts that companies do not have and that are scarce in the market. For these corporations, making the digital transition has turned data into a new productive factor, while some companies, including those in the digital economy and telecommunications, have monetized data, turning it into a source of value generation, in a new line of product.
On the one hand, this situation can be explained due to the traditional lack of agility of our educational system to respond to changes in demand, although it is no less true that the current configuration of the paradigm and its rapid transformations also make it difficult to train professionals of this profile, while for certain professionals, updating and acquiring this type of skills has become a necessary condition to continue exercising their profession. In this sense, we will agree that the framework of competencies that define a good number of professions is changing at a dizzying rate without the study plans being at the same rate, so that it would not be strange to find a good number of graduates with high employability issues in a constantly changing world.
Perhaps the fact that massive data analysis and its analysis techniques have a diverse origin in which analysis strategies and techniques from Engineering, Mathematics, Statistics and Economics are also causes that explain the slow response to the absence of a defined paradigm, since rather a new paradigm is being generated by hybridization of all these disciplines.
This phenomenon has a lot to do with the relative heterogeneity of the new training offers that are trying to respond to this demand. Thus, depending on the approach, we find postgraduate programs in Big Data with a Business Intelligence profile, Data Science programs with a marked statistical character, programs focused on Machine Learning and Data Mining and programs focused on architecture. Big Data, depending on the focus or even the faculty from which the project emanates. However, the most recent approaches are multidisciplinary approaches in which not only the principles of programming, storage, capture and big data architecture are important, but in these programs, econometrics, machine learning or data mining, configure a new paradigm thanks to the exploration of intersections and complementarities.
With all this, in the national context a diverse postgraduate offer is configured both in the official or proper character, as well as in the profiles. Thus, there are a good number of postgraduate offers in the field of Business Intelligence or Business Intelligence, oriented so that those responsible for designing business strategy understand the main elements of data-based management, without pretending that these graduates are Data analysts or capable of designing a Big Data strategy, but who are at least capable of appreciating its economic importance. A good number of Business Schools and Universities have launched to offer this type of studies, to some extent substitutes for the old MBA for which it is not necessary to have advanced technical or mathematical training, since the analytics is based on the interpretation of results from black box programs.
Another group of programs tries to go one step further and hybridize economic or business studies with data analysis, with an emphasis on learning techniques and in which technical knowledge of programming or Big Data architecture is subject to applicability. An example of this type is the Master in Data Science from Pompeu Fabra, UAB, Alcalá or the University of Barcelona or the Master in Economics, Finance and Computing UHU-UNIA.
A third group usually mixes Data Science and Big Data, although with different weighting between these two areas, as happens, for example, with the Master's degree in Big Data Analytics from Carlos III, and the Master's degree in Big Data from the University Complutense of Madrid. A last group tries to provide a comprehensive training in the range of statistical tools behind data science. This is the case of the Master in Statistics for Data Science at the Carlos III University.
In Andalusia, there are currently coexisting, in addition to the aforementioned Official Master's Degree in Economics, Finance and Computing from UNIA, an Own Master's Degree in Data Science and Big Data from the University of Seville, a Master's Degree for Industry 4.0 at the University of Córdoba, an own Master's Degree Big Data and Business Analytics at UPO, an own Master's Degree in Big Data from the University of Malaga and an Official Master's Degree in Data Science and Computer Engineering from the University of Granada.
To all this we must add the offer of private online universities (UOC, UNIR), which have identified a niche of high demand due to the high employability of this type of specialists.
Nor are the most reputable international universities alien to these trends, which have been agile in responding to this latent demand both in person and online (the Chicago, Harvard, Stanford, or Luiss programs are a good example of this. ).
The proposal that is presented does not try to compete in the field of Business Intelligence, Digital Marketing programs, or in the field of Economics and Finance, but to become a multidisciplinary offer in which the most recent developments and border, in Big Data from the perspective of Languages, Systems and information technologies as regards architecture and processing, and from the perspective of Machine Learning and Statistics for data analysis. That is why the proposal starts from this idea, combining specialists from Engineering, Mathematics and Economics.
From this perspective, it is a unique proposal that does not collide with others that tend to be "biased" by the predominance of a certain paradigm, without taking into account the hybridization that is taking place in the new Big Data paradigm.
From the demand point of view, this program responds, on the one hand, to those graduates who wish to acquire a professional profile in which Big Data competencies exponentially increase their employability and the possibility of entrepreneurship. In addition, and alongside these, there is a good number of professionals who are pushed by their companies to acquire this type of skills as a way to facilitate their digital transformation, or their transition to a culture of data-based decision making.
In both cases and given that the target audience for this program comes from the STEM sector, their high employability, advises that the virtual teaching modality is the one that best suits this type of students who require flexibility to be able to study a postgraduate academic offer.CurriculumThe Own Master in Big Data has a total of 60 ECTS, distributed in the following Modules:Module I: Fundamentals and Big Data Architecture (18 ECTS)
Module II: Advanced Scientific Computing (12 ECTS)
Module III: Machine Learning (12 ECTS)
Module IV: Econometrics (6 ECTS)
Module V: Recommendation Systems (3 ECTS)
Module VI: Business Intelligence (3 ECTS)
Module VII: Master's Final Project (6 ECTS)Access requirementsThe degree is aimed at graduates in Bachelor's and Bachelor's degrees related to the STEM sector, and especially to graduates in Engineering, Mathematics, Physics, Statistics, and students who already have a master's degree related to data analysis or engineering. However, and given the transversality of the program, profiles of graduates in other related degrees (as well as those equivalent in other countries) who either by their curriculum or by their professional experience have the basic knowledge necessary to be able to follow a course of these characteristics.Admission criteriaThe following items will be selection criteria:Academic record. (GMAT is not mandatory but will be valued)
Professional experience in architecture, extraction, data analysis and related subjects.
Knowledge of languages.