Other profiles might be considered provided they guarantee solid foundations in Mathematics and Computer Science as to enable the candidate to follow the programme.
The master's degree in Data Science is offered to students from all over the world. Candidates must have a good comprehension, oral and written expression in English. Candidates must also be motivated to discover knowledge from data in the fascinating world of sciences and technologies.
Four semesters. (120 ECTS)
Data drives the world. The primary social and economic value of modern societies is knowledge. For this reason, mastering information technologies to extract knowledge from structured or unstructured data (coming from databases, websites, social networks, sensors, documents, images, videos, etc.) is indispensable for the development of individuals and the society in the current data-driven economy.
The European Political Strategy Centre (EPSC) stated that “Data is rapidly becoming the lifeblood of the global economy. It represents a key new type of economic asset. Those that know how to use it have a decisive competitive advantage in this interconnected world, through raising performance, offering more user-centric products and services, fostering innovation—often leaving decades-old competitors behind. (. . . ) Data analytics will soon be indispensable to any economic activity and decision-making process, both public and private.”
This is trend is accelerating with the arrival of the so-called big data paradigm, as data are being generated by numerous devices around us at all times: digital processes produce data; systems, sensors and mobile devices capture them and organizations eventually exploit (by means of advanced data analytics) such data to get the most knowledge out of it.
While attention to the importance of data is not new, the predicted economic value that can be extracted from data remains largely unrealized. There are multiple factors responsible for the non-realisation of this potential, but a crucial factor is that unlocking value from raw data is hard: before any newly acquired data becomes useful, it must be preprocessed, integrated with existing data, cleaned from errors, appropriately stored, and prepared for analysis. Therefore, if quality is defined by “fitness for use”, the quality (and thus, the value) of data is very low at its inception and a great deal of management and processing effort is required to increase this.
As a result, we talk today about the so-called data value creation chain, which encompasses multiple disciplines and people with different roles, under the umbrella of Data Science (DS). DS is defined as the scientific “interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.” Nowadays, there is no doubt DS is at the core of the global economy. Accordingly, the European Commission (EC) published an ambitious strategy for the data economy called Digital Single Market (https://ec.europa.eu/digital-single-market/en/towards-thriving-data-driven-economy), which expresses the commitment of the EC, industry and academia partners to build a data-driven economy across Europe, mastering the generation of value from Big Data and creating a significant competitive advantage for European industry, boosting economic growth and jobs.
Not everything is positive about the data value creation chain. Several reports warn of the current gap in the capacities of the professionals in the sector. Along these lines, the EC expressed recent concern about the shortage of personnel with innovation skills in the field of DS. They estimated 3.5 million job vacancies due to an offer relatively rigid education while demand grows in all sectors professionals. These vacant positions represent 30.2% of the total demand in the sector.
As stated by the Big Data Value Association (BDVA) in their Strategic Research & Innovation Agenda, “mastering the creation of value from big data will impact the competitiveness of companies and will result in economic growth and jobs for Europe.” Accordingly, the BDVA states that Europe has a need for an up-front investment in research and education to be able to cover its own DS industrial needs. These needs span the complete data lifecycle throughout the data value creation chain: i.e., (1) rigorous data management skills to deal with big data, (2) strong data analysis skills to extract knowledge from data and (3) knowledge of the application domain.
The Data Science master is a unique programme with a strong international perspective devised to meet all the requirements identified by key players in the field of DS. Relevantly, this master is fully aligned with the Big Data Management and Analytics (BDMA) Erasmus Mundus master, funded by the EC and currently a world-wide referent programme in the field of DS.
The curriculum of the FIB Master's Degree in Data Science was approved by the Faculty Board on 1st July, 2020. It is adapted to the European Higher Education Area (EHEA) and has a 120 ECTS credits:
The University Master's Degree in Data Science from the UPC is structured in 4 semesters. The first semester covers 30 compulsory ECTS; the second semester covers the remaining 24 compulsory ECTS and 6 elective ECTS; the third semester must cover the remaining 30 optional ECTS. The fourth and final semester is fully devoted to the master thesis.
Thus, the compulsory training requires 54 ECTS (equivalent to 9 subjects of 6 ECTS) divided into 3 fields:
The following is the presentation of the structure of the master's study plan:
The elective training is structured in 36 ECTS. The 36 ECTS must be completed from the following offered tracks:
The deep dive in specific aspects of Data Science track deepens in advanced aspects of data management and data analysis. The applications of Data Science for specific domains track focuses on Data Science techniques specific for popular domains of application, which require specific pre-processing, management and analysis of specific data. The deep dive track is meant to get specialized in advanced techniques, while the applications track is meant to get specialized in specific domains. Finally, the Innovation and Research track delves into the connection of Data Science with business innovation and research. Courses in the innovation and research track focuses on fostering the required traversal skills to meet the high level of innovation necessary in the professional field of Data Science.
Students can choose courses from the abovementioned tracks to fulfill the elective training. However, the following maximum of ECTS per track is set:
Data science has emerged as a significant field at the crossroads between science and technology. Data scientists help businesses, government, and society to leverage oceans of available data using very powerful and relatively cost-effective analytic technology. The ability to achieve the full potential of data analytics requires not just data, tools, and infrastructure, but also quantitative skills to traverse the huge mountains of data.
Graduates of this specialization may find employment in a range of sectors:
All of the internationally recognized journals and consulting institutions affirm the paramount importance of data mining, business intelligence, big data analytics and data science in their reports for the 21st century:
The market for BI platforms will remain one of the fastest growing software markets.Gartner
The market size grew to 14 billion US dollars in 2014, compared to 8.8 billion US dollars in 2008The analyst firm Forrester
The field of data management and analysis is estimated to be worth more than 100 billion US dollars and grows by nearly 10% per year.The Economist
The shortage of professionals in such fields is deemed to be 4.4 million in US for 2015IBM
The McKinsey Global Institute, in its May 2011 report, states big data as the next frontier for innovation, competition, and productivity. In addition, it is worth mentioning that big data professionals have not been affected by the current economic crisis and are considered to be a top priority of CEOs everywhere.
The admission period to start the Master programme in September 2021, is open from 25 February to 5 June, 2021.
The admission period is divided into two periods:
The notification from 11 June will include a waiting list with all the candidates whose position in the candidates list is higher than the number of places offered in the Master. The list will be ordered by candidates access mark. The candidates in that list can become admitted if some of the candidates firstly admitted decide to give up their seat.
Admitted candidates must accept their admission and confirm it with the payment of the allocation fee before 17 June, 2021. After that day, we will understand that they give up their seat, so their admission will be revoked and the place assigned to the following candidate in the waiting list.
The official list of accepted candidates will be published on 18 June, 2021, on the FIB website, in the "Enrollment" section of each Master programme.
Candidates must provide proof of their English proficiency, with at least a B2 level of the Common European Framework of Reference for Languages (or equivalent).
IMPORTANT: All documents issued outside Spain or in non-Spanish-speaking countries must be translated into Spanish or Catalan. All documents issued in non-EU countries must be legalised and bear the corresponding apostille.
The Academic Committee is in charge of the admission of the candidates. The criteria are: Academic Transcripts (60%), Relevance of the Bachelor (20%), Background and professional experience (5%) and Motivation Letter (15%).
For a successful development of the studies leading to the title of Master's Degree in Data Science taught at UPC, the admission profile must correspond to the following personal and academic characteristics:
Since Data Science sits in the confluence of Computer Science and Mathematics, the main recommended entry profiles are:
However, there are several other degrees that would allow to successfully pursue a master's degree in Data Science. Specifically, any degree that guarantees a solid knowledge in the Computer Science and Mathematical technical competences previously mentioned. For reference, the following degrees typically meet the expected technical competences:
Given the diversity of degrees in these areas, and since this master's degree does not consider training complements, the academic committee will check the bachelor syllabus of the applicant and assess whether the study plan followed adequately covers the Mathematics and Computer Science technical skills required in order to be admitted.
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