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:

specific 2024 presentation for MDS
videos from 2024 (General | MDS).

Presentation

About

The master degree in Data Science equips graduates with solid foundations and hands-on experience in fundamental aspects of data management and analysis to extract hidden knowledge from structured and unstructured big data and in building adaptive analytical systems that are able to exploit that knowledge in modern organizations. In particular, graduate students will be prepared to address the new challenges of the so-called data-driven society and develop systems based on data to tackle relevant topics such as fraud detection, bioinformatics and e-health, information extraction from highly unstructured data, real time analysis of sensor data and social networks, customer relationship management, etc. 
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

The master’s degree in Data Science is an interdisciplinary programme that bridges Computer Science and Mathematics. The recommended entry profiles are the following:

  • 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

English

Duration

Four semesters. (120 ECTS)

Face-to-face

Workload

  • Full-time: 20h lectures, 20h personal work per week
  • Part-time: half full-time workload

Fees

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:

Mandatory
Elective
Master's Thesis

 

Semester 1

Statistical Inference and Modelling
(SIM - 6 ECTS)

Algorithms, Data Structures and Databases
(ADSDB - 6 ECTS)

Data Warehousing
(DW - 6 ECTS)

Multivariate Analysis
(MVA - 6 ECTS)

Process-oriented Data Science
(PODS - 6 ECTS)

Semester 2

Big Data Management
(BDM - 6 ECTS)

Semantic Data Management
(SDM - 6 ECTS)

Machine Learning
(ML - 6 ECTS)

Mining Unstructured Data
(MUD - 6 ECTS)

Elective Course
(6 ECTS)

Semester 3

Elective Courses
(30 ECTS)

Semester 4

Master's Thesis
(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

Advanced Statistical Modeling
(ASM - 6 ECTS)

Algorithmics for Data Mining
(ADM - 6 ECTS)

Optimization Techniques for Data Mining
(OTDM - 6 ECTS)

Advanced Machine Learning
(AML - 6 ECTS)

Advanced Multivariate Analysis
(AMA - 6 ECTS)

Information Retrieval and Recommender Systems
(IRRS - 6 ECTS)

Complex and Social Networks
(CSN - 6 ECTS)

Data Analysis and Knowledge Discovery
(DAKD - 6 ECTS)

Cloud Computing and Big Data Analytics
(CCBDA - 6 ECTS)

Applications of Data Science for Specific Domains

Bioinformatics and Statistical Genetics
(BSG - 6 ECTS)

Human Language Engineering
(HLE - 4,5 ECTS)

Data Management for Transportation
(DMT - 4 ECTS)

Innovation and Research

Viability of Business Projects
(VBP - 6 ECTS)

Debates on Ethics of Data Science
(DEDS- 3 ECTS)

Interdisciplinary Innovation Project
(I2P- 6 ECTS)

Techniques and Methodology of Innovation and Research in Informatics
(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

Using big data reduces fraud and errors and boosts the collection of tax revenues.

Genomics

The prediction of classical epidemiological models can be greatly improved using genomic data.

Healthcare

Using big data analytic techniques drives efficiency and quality. In the developed economies of Europe, government administrators could save more than 100 billion euros in operational efficiency improvements alone, using big data analytics (McKinsey Global Institute, 2011).

Transparency in smart cities

By simply making data and analytics more easily available to relevant stakeholders in a timely manner, huge added value could be created.

Personal Services

Using data mining techniques for services enabled by personal-location data could capture 600 billion euros in consumer surplus (McKinsey Global Institute, 2011).

Business

Big data analytics will become a key basis for competition and growth of individual firms and allow new products and services to be offered.

Individual entrepreneurship

Leading companies are using data collection and analysis to conduct controlled experiments that lead to better management decisions; others are using data for real-time forecasting to adjust their business levers just in time. Big data analysis allows narrower segmentation of customers and therefore much more precisely tailored products and services. Sophisticated analytics can substantially improve decision making. Big data analytics fosters the development of the next generation of products and services, i.e., manufacturers can use data obtained from sensors embedded in products to create innovative after-sales services such as proactive maintenance. It is obvious that the efficient usage of data mining techniques will substantially increase gains across sectors from computer and electronic products and information to finance and insurance.  

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 14 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

Information about prices and fees for the UPC's official master's degrees can be found at Fees and payment options.

Our masters have specific grants and scholarships.

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

  1. Curriculum Vitae
  2. DNI, NIE or passport
  3. 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.
  4. 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.
  5. English knowledge certificate (minimum B2 of Common European Framework of Reference). The certificate must be included in the table (in Catalan language) published with the decree REU/2197/2024 issued by the Ministry of Research and Universities of the Catalan Government.
  6. 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.
  7. If the degree that gives access to the master was obtained in a foreign country, official statement of equivalence of the average grade obtained.
  8. 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:

  • Knowledge in algorithmics, data structures, programming and databases equivalent to, at least, that of a degree with a minor in Computer Science.
  • 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.
  • Comprehension, oral and written expression in English (B2 level or equivalent).

Abilities:

  • Aptitude for study and organize your learning.
  • Advanced skills for logical reasoning and problem solving.

Capabilities:

  • Ability to analyse and synthesise information.
  • Ability to argue, reason and express ideas.

Attitudes:

  • Organised, curious, enterprising person willing to apply knowledge to real situations.
  • Creative and innovative capacity in front of the evolution of technological advances.
  • 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.
 

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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.

Görkem Çamli - Bachelor in Computer Science - Bilkent University (Turkey)


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!

Victor Herrero - Degree in Informatics Engineering at FIB

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.

Jaime Andres Merino - Technical Computer Systems Engineering at Universidad Politecnica de Madrid

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