Master in Data Science
The pre-enrolment is open from 26 February to 26 May 2024. It is divided into two periods:
- 1st period. From 26 February to 18 April.
- 2nd period. From 22 April to 26 May.
Do you want to attend our information sessions? Sign up for them!
All general sessions will include MEI, MIRI, MAI and MDS.
The sessions will be held in English, except for the specific MEI session, which will be in Catalan/Spanish.
MEI: Master in Informatics Engineering; MIRI: Master in Innovation and Research in Informatics; MAI: Master in Artificial Intelligence; MDS: Master in Data Science
Check our general information session presentation and vídeo of 2024. Slides and videos are updated as long as the information sessions take place. If you want to check last year's find them below:
Presentation
About
The master degree in Data Science has been designed with the objective of training highly qualified professionals who will be provided with fundamental understanding and the required competences (that is, the combination of knowledge and skills) to apply their learning in the demanding field of Data Science. Therefore, this program will generate highly innovative, interdisciplinary professionals with a strong research-oriented perspective, specially prepared to face the challenges identified by industry.
Target
- Computer Science and equivalent degrees.
- Mathematics and equivalent degrees.
- Classic Engineering degrees or equivalent degrees with a strong mathematical background covering in depth algebra, calculus, statistics and with at least a minor in Computer Science.
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.
Language
Duration
Four semesters. (120 ECTS)
Face-to-face
Workload
- Full-time: 20h lectures, 20h personal work per week
- Part-time: half full-time workload
Why this master's degree?
Towards a thrilling data-driven economy
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.
Skill gap
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.
Curriculum
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:
- 54 compulsory credits
- 36 elective credits
- 30 credits of Master's Thesis
Curriculum structure
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:
- Data Science Fundamentals (12 ECTS): Statistical Inference and Modeling (SIM), and Algorithmics, Data Structures and Databases (ADSDB).
- Data management (18 ECTS): Data Warehousing (DW), Big Data Management (BDM) and Semantic Data Management (SDM).
- Data analytics (24 ECTS): Multivariate Analysis (MVA), Process-oriented Data Science (PODS), Machine Learning (ML) and Mining Unstructured Data (MUD).
The following is the presentation of the structure of the master's study plan:
Semester 1
(SIM - 6 ECTS)
(ADSDB - 6 ECTS)
(DW - 6 ECTS)
(MVA - 6 ECTS)
(PODS - 6 ECTS)
Semester 2
(BDM - 6 ECTS)
(SDM - 6 ECTS)
(ML - 6 ECTS)
(MUD - 6 ECTS)
(6 ECTS)
Semester 3
(30 ECTS)
Semester 4
(30 ECTS)
Elective Courses
The elective training is structured in 36 ECTS. The 36 ECTS must be completed from the following offered tracks:
- Deep Dive in Specific Aspects of Data Science
- Applications of Data Science for Specific Domains
- Innovation and Research
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 fulfil the elective training. However, the following maximum of ECTS per track is set:
- 24 ECTS on the applications of Data Science for specific domains track,
- 15 ECTS on the innovation and research track,
- 36 ECTS on the Deep Dive in specific aspects of Data Science (no limit).
Deep Dive in Specific Aspects of Data Science
(ASM - 6 ECTS)
(ADM - 6 ECTS)
(OTDM - 6 ECTS)
(AML - 6 ECTS)
(AMA - 6 ECTS)
(IRRS - 6 ECTS)
(CSN - 6 ECTS)
(DAKD - 6 ECTS)
(CCBDA - 6 ECTS)
Applications of Data Science for Specific Domains
(BSG - 6 ECTS)
(HLE - 4,5 ECTS)
(DMT - 4 ECTS)
Innovation and Research
(VBP - 6 ECTS)
(DEDS- 3 ECTS)
(I2P- 6 ECTS)
(TMIRI - 6 ECTS)
Employment opportunities
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:
Government
Genomics
Healthcare
Transparency in smart cities
Personal Services
Business
Individual entrepreneurship
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 2008
The 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 2015
IBM
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.
Admission
Calendar
The admission period to start the Master programme in September 2024, is open from 26 February to 26 May 2024.
The admission period is divided into two periods:
- 1st period. From 26 February to 18 April. The decision on applications will be announced no later than 2 May.
- 2nd period. From 22 April to 26 May. The decision on applications will be announced no later than 9 June.
The decision may be that the candidate has been admitted, or that the candidate has been rejected or, in the case of the 1st period, that the decision has been postponed until the following period.
The notification from 9 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 should be done before the date indicated in the admission message. 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 on the waiting list.
The official list of accepted candidates will be published on July 2024, on the FIB website, in the "Enrolment" section of each Master programme.
Fees and payment options
Requirements
UPC
The admission requirements for the UPC's official masters can be found at What are the requirements to enroll in a master's degree?
Language
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).
Required Documents
- Curriculum Vitae
- DNI, NIE or passport
- Academic Personal Certificate. The certificate must detail the number of hours and credits studied and must include the grade awarded for each subject. This certificate also must contain information on the mark scheme and how grades are awarded.
- Diploma (or similar document). This document must confirm that the duration of the degree was at least three years and that is it a valid precursor to studies at postgraduate level (master’s degree) in that country. If that is not possible, it must indicate the level of higher education that the degree gives access to in the country where it was awarded.
- English knowledge Certificate (minimum: B2 of Common European Framework of Reference for Languages)
- If the applicant holds other qualifications related to the subject area of the Master’s Degree course applied for and that are different from those given to meet entrance requirements, it is necessary to include the original certificates and copy (or official copy) with the application.
- If the degree that gives access to the master was obtained in a foreign country, official statement of equivalence of the average grade obtained.
- Motivation letter of at most two pages containing a brief description of your trajectory, academic interests and reasons for applying to this specific Master. Please detail also your career objectives.
IMPORTANT:
All the academic documents issued by Pakistani institutions must be attached together with the electronic validation of the Higher Education Commission of Pakistan (HEC)
All the academic documents issued by Chinese institutions must be attached together with the China Credentials Verification of the Center for Student Services and Development (CSSD)
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.
Selection Criteria
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%).
Expected Applicant Profile
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:
Technical Competences:
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Knowledge in algorithmics, data structures, programming and databases equivalent to, at least, that of a degree with a minor in Computer Science.
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Knowledge in algebra, calculus and statistics equivalent to the fundamental knowledge obtained in Computer Science, or in the first years of the main degrees of Engineering.
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Comprehension, oral and written expression in English (B2 level or equivalent).
Abilities:
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Aptitude for study and organize your learning.
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Advanced skills for logical reasoning and problem solving.
Capabilities:
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Ability to analyse and synthesise information.
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Ability to argue, reason and express ideas.
Attitudes:
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Organised, curious, enterprising person willing to apply knowledge to real situations.
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Creative and innovative capacity in front of the evolution of technological advances.
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Interest in Information and Communication Technologies.
Recommended Bachelor Degrees
Since Data Science sits in the confluence of Computer Science and Mathematics, the main recommended entry profiles are:
- Students with a degree in Computer Science.
- Students with a degree in Mathematics.
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:
- Students with a degree in Physics or equivalent.
- Students with a degree in Statistics or equivalent.
- Students with a degree in Telecommunication Sciences and Technologies, Telecommunication Technology and Services Engineering, Electronic Telecommunication Engineering or equivalent.
- Students with a degree in Civil Engineering or equivalent.
- Students with a degree in Engineering in Industrial Technologies, Industrial Electronics and Automation or equivalent.
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.
Pre-enrolment
You’ll need to complete and submit our online application form to apply for a master’s degree. Click the button below to begin the application process
Pre-enrolment
Faculty
Professor in charge
Professors
- Abello Gamazo, Alberto
- Arias Vicente, Marta
- Arjona Martinez, Jamie
- Arratia Quesada, Argimiro
- Balcázar Navarro, Jose Luis
- Belanche Muñoz, Luis Antonio
- Berral García, Josep Lluís
- Bilalli, Besim
- Castro Pérez, Jordi
- Conti, Dante
- Del Rey Juarez, Santiago
- Delicado Useros, Pedro
- Diaz Boladeras, Marta
- Duarte López, Ariel
- Eguiguren Huerta, Marcos
- Escolano Peinado, Carlos
- Ferrer Cancho, Ramon
- Franquet Fàbregas, Josep
- Gibert Oliveras, Karina
- Gómez Sebastià, Ignasi
- Heredia Cervera, F. Javier
- Hinjos García, Daniel
- Iñigo Robles, Ruth
- Jovanovic, Petar
- König, Caroline
- Larrosa Bondia, Francisco Javier
- Linares Herreros, Mari Paz
- Martín Muñoz, Mario
- Martínez Fernández, Silverio Juan
- Martínez Martínez, Matías Sebastián
- Medina Herrera, Salvador
- Montero Mercadé, Lidia
- Pocull Prous, Ruben
- Poveda Cabanillas, Clara
- Puig Oriol, Xavier
- Queralt Calafat, Anna
- Rafieian, Bardia
- Ramirez Mitjans, Sergi
- Rodríguez Pereira, Jessica
- Romero Moral, Oscar
- Serral Gracià, René
- Sierra Santibañez, Maria Josefina
- Sánchez Espigares, Jose Antonio
- Toribio Gonzalez, Angel
- Torres Viñals, Jordi
- Turmo Borrás, Jordi
- Valiente Feruglio, Gabriel
- Vellido Alcacena, Alfredo
- Vázquez Alcocer, Pere Pau
Double Degree
Student Testimonials
For me, the main strengths of this master’s were its strong focus on the Data Science field and vast opportunities to be indifferent academic and industrial settings. In many Master’s programs, you have to take generic departmental classes before you even start to learn your specialization, whereas all the classes in MIRI Data Science were focused on Data Science which meant more time to focus on learning your specialization. The focus in the classes varies from applied study, theory, and research and many of them encourage teamwork. With the electives, you can either get a deeper knowledge in one specific Data Science area or explore as many as you like.The course loads were not very heavy so it gives time to focus on other opportunities to explore.
What I liked the most about this degree was that the opportunities were there for you to have them. I was lucky enough to be part of a research team, did internships both in Barcelona and in London, and went to an Erasmus to EPFL in Switzerland for one semester.
Also, in my cohort, the student body was diverse with people from different backgrounds and countries which created a unique experience where we all learned from each other. It was a nice two years where I enjoyed studying with many nice people, learn plenty about the Data Science field, and explored many academic and industrial opportunities.
What I really like about this master is that it combines studies on data analysis and data management with the purpose of approaching both worlds; a demanding need for today's business of employing people capable of merging the very best of the two.
The first year already gave me a very strong background to start with, and the second year allowed me to choose on what fields of my interest I wanted to specialize.
My opinion is that it might not be a soft trip and you might need to work hard, but the things you learn along way are really worth it!
Looking for proper specialization in Data Analysis was very difficult to find. Now that I'm taking on my Master Thesis, I can say that this programme, was a very good choice. It combines advanced theoretical university teachings with hands-on programming. More specifically, the combination of statistics and computing capabilities learnt here will be very difficult to beat in the market. With the techniques acquired, I would say we are ready to tackle every single problem in data analysis I can think of, at the time when everything in the world is being measured and turned into digital data. So the possibililities are just there for you to take. But as there is no free lunch in economy, be prepared, you will need to work very hard.
Click the button below to begin the application process
Pre-enrolment To pre-enroll, you will need to register online
Once you've been admitted, you will find all the academic information as how to register, academic calendar, schedule of classes and exams, etc. at FIB Website.