DATA SCIENCE

OBJECTIVES

The main objective of the Master in Data Science is to prepare highly qualified professionals, particularly in the analysis of large volumes of data. The course is designed to provide solid knowledge in the areas of statistical analysis and computer science. Data Science lies at the intersection of these two areas of knowledge that the Data Scientist must master. It is this virtuous combination of skills that distinguishes this course from other offerings. In addition to solid knowledge, this Master also conveys practical and applied knowledge in Data Science through laboratory classes, practical assignments and projects in collaboration with companies that have real problems that require Data Science methods.

STRUCTURE

This Master is composed by:

• a 60 ECTS credits study program, in the first year, conferring a Specialization in Data Science;
• a scientific Dissertation, of original scientific nature and specially made for this purpose, corresponding to 48 ECTS credits, complemented by 12 ECTS credits obtained with a compulsory course in the area of Management and another optional course. The Dissertation’s approval in a public defense allows to obtain the degree of Master’s degree in Data Science.

STUDY PLAN

Click here to view the complete version of the latest curricular plan.

ADMISSION REQUIREMENTS

• Bachelor’s holders or holders of a foreign university degree in the conditions imposed by law, in any of the following scientific areas: Computer Science, Mathematics, Economics, Engineering, Physics, Biology and similar areas or equivalent by law;
• Holders of an academic, scientific or professional curriculum, in any of the above areas, in the conditions imposed by law;
• Domain of the English language.

SELECTION CRITERIA

Candidates who have a Bachelor’s degree in the area of ​​the study cycle, according to the criteria defined by the Scientific Committee of the Master, will be placed first.

Placing will be done according to the following criteria and subcriteria:

• Academic curriculum (area of ​​training and average obtained) (60%)
  • Sub-criterion 1: average, weighted with the adequacy of the degree in a coefficient between 0 and 1 (50%). Bachelor’s degrees in the areas of computer science / computer engineering, mathematics, statistics, data analysis, artificial intelligence, physics, physical engineering, electrotechnical engineering and industrial or equivalent management engineering have a weighting of 1. Bachelor’s degree with a solid background in statistics / mathematics or computing such as mechanical engineering, economics, management, IT management have weighted 0.9. Graduates with some training in statistics or in computing such as biology, (engineering) chemistry and other engineering have a weighting of 0.8. Other degrees will be analyzed case by case and the weighting of any case can be changed depending on the analysis of the academic course of the specific candidate taking into account the Curricular Units in the areas of computation and statistics / mathematics.
  • Sub-criterion 2: complementary training in the field, including lectures and non-degree courses, such as other master’s degrees, short or long postgraduates, and duly certified short courses (10%).
• Scientific curriculum and professional experience (40%)
  • Sub-criterion 1: technical and / or scientific publications and communications (20%)
  • Sub-criterion 2: relevant professional experience in companies, participation in research projects or internships in the area of ​​the Master weighted with relevance of the area (20%)

Tiebreaker:
In the event of a tie, the result of an interview will be used as a tie-breaking criterion.

CAREER PROSPECTS

Numerous market studies have alerted to the growing need for professionals capable of analyzing the volume of data that our society has been producing exponentially. Several technological advances have contributed to this growth in the volume of data available. The reduction of the cost of numerous sensors, the advance of the “computerization” of the great majority of human activities, the phenomenon known as Internet of Things (IoT), among other factors, have been the origin of this growth. Increasingly, the vast majority of human activities are in some way recorded on computer media. This huge amount of data “hides” useful information about organizations and their activities. Being able to discover this information from this large data set is therefore a competitive advantage that most organizations have already identified as the key to success. For this to be possible, taking into account the volume of data, it becomes necessary computational tools as well as professionals able to develop and use them efficiently. The Master in Data Science aims to train this type of professionals and thus help fill the recognized gaps at this level in terms of the labor force currently available in the labor market, as alerted by several studies and business entities.