BROCHURE |
 |
PRE-ENROLMENT AND ENROLMENT |
Calendar |
MASTER'S PROJECT |
REGULATIONS |
QUALITY |
Official documentation of the program |
Suggestion Box |
Inter-university Master's programme organised in collaboration with the University of Cantabria
8th Edition. From October 1st, 2024 to May 2025
The Menéndez Pelayo International University (UIMP) and the University of Cantabria (UC) have planned, through an academic alliance, the program for the Official Master's Degree in Data Science.
The aim of the Master's programme is to provide students with the necessary overview and techniques in Data Science to deal with the challenges linked to Big Data and associated tools in an Open Science environment.
It is therefore hoped that the experience acquired by the different research groups collaborating on this Master's programme is conveyed to students, covering the different areas based on their experience on many projects.
After taking this Master's programme, students are expected to be able to join a specialised professional setting, either in research or operational activities. To achieve this, placements and final Master's dissertations linked to companies and/or research groups shall be endorsed, as well as intensive work with real useful data in different specific areas via so-called Data labs.
Regular Pre-Enrolment Period: March 11 to May 30, 2024 (until 23:59 Madrid time).
Special Pre-Enrolment Period: July 15th to September 10th, 2024 (until 23:59 Madrid time).
A special enrolment period will be opened to fill slots that may have remained vacant after the regular enrolment period.
Enquiries regarding the pre-enrolment process should be addressed to: preinscripcion.posgrado@uimp.es
Regular Enrolment Period: From June 25, 2024, within 10 calendar days of receipt of the notification of admission.
Special Enrolment Period: Within 10 calendar days of receipt of the notification of admission.
Second year students and those with pending credits: from September, after the previous course records are closed. The student secretariat will inform those students who are going to take their Master’s second course and hose with pending credits of the specific dates for enrolment.
Submission of documentation: from the time of enrolment until the first two weeks of the course.
Credit recognition application: from the time of enrolment until the first two weeks of the course.
Enrolment cancellation:
Period with return of tuition fees (up to 50% of the tuition fees total): Cancellation of enrollment within the first 10 days of the course will have academic and economic effects.
Period with no return of the tuition fees: after the 10th day of the course, cancellation of enrollment will only have academic effects.
Enquiries regarding the enrolment process should be addressed to: alumnos.posgrado@uimp.es
Additional information can be found at this link
Lara Lloret Iglesias, Postdoctoral Researcher, Cantabria Institute of Physics (IFCA), CSIC-UC
Francisco Matorras Weinig, Department Chair of Atomic, Molecular and Nuclear Physics, University of Cantabria (UC)
Contact email: info-masterdatascience@listas.csic.es
Spanish (some seminars may be delivered in English)
Admission of candidates will be decided by the Academic Committee for the Master's according to the conditions for access and specific requirements for admission to the program with the objective of not surpassing the 25 spaces established as the course maximum.
Faculty of Sciences, University of Cantabria
Avenida de Los Castros, 48. 39005 Santander (Cantabria), Spain
Classes will run from October to May, Monday to Friday, 3:30-7:30pm.
The Master in Data Science has a teaching load of 60 ECTS credits that is spread over two four-month terms (one academic year).
The programme is structured into 36 obligatory credits, 18 elective credits and 6 Final Master's Dissertation credits. The programme offers four specialities.
The syllabus structure for the four specialities is as follows:
Academic year 2024-25 , only the specialization in Intelligence in Data Science / Data Science Analytics is offered
This speciality aims to provide students with direct contact with technologies, mainly in computer engineering, which enable the deployment of data analysis tools and the development and introduction of new solutions.
Module I - Foundations (30 ECTS): subjects 102263 to 102267
Module II - Specialisation - Data Science Analytics (12 ECTS): subjects 102268 to 102270
Module III - Professional Context (6 ECTS): subjects 102277 and 102278
Module IV - Professional Guidance (6 ECTS) - Students must select at least one of the following six-credit elective subjects: subjects 102279 to 102285
Module V - Final Master's Dissertation - Data Science Analytics: subject 102286
This speciality aims to provide students with core knowledge in different machine learning methodologies and techniques so that they know how to apply them critically to real-world problems, including text and web mining.
Module I - Foundations (30 ECTS): subjects 102263 to 102267
Module II - Specialisation - Data Science Engineering (12 ECTS): subjects 102271 to 102273
Module III - Professional Context (6 ECTS): subjects 102277 and 102278
Module IV - Professional Guidance (6 ECTS) - Students must select at least one of the following six-credit elective subjects: subjects 102279 to 102285
Module V - Final Master's Dissertation - Data Science Engineering: subject 102287
This speciality aims to provide students with direct contact with technologies used to implement data repositories and their subsequent operation.
Module I - Foundations (30 ECTS): subjects 102263 to 102267
Module II - Specialisation - Open Data Management (12 ECTS): subjects 102274 to 102276
Module III - Professional Context (6 ECTS): subjects 102277 and 102278
Module IV - Professional Guidance (6 ECTS) - Students must select at least one of the following six-credit elective subjects: subjects 102279 to 102285
Module V - Final Master's Dissertation - Open Data Management: subject 102288
This speciality has a cross-cutting profile and provides students with the freedom to choose three of the nine subjects offered on the Specialisation Module.
Module I - Foundations (30 ECTS): subjects 102263 to 102267
Module II - Specialisation (12 ECTS) - Students must choose three subjects: subjects 102268 to 102276
Module III - Professional Context (6 ECTS): subjects 102277 and 102278
Module IV - Professional Guidance (6 ECTS) - Students must select at least one of the following six-credit elective subjects: subjects 102279 to 102285
Module V - Final Master's Dissertation - Data Science: subject 102289
AF1 - Participation and attendance at lectures and seminars
AF2 - Doing computational and data analysis practicals
AF3 - Developing guided projects
AF4 - Participation in case studies at companies or research centres
AF5 - External placements
AF6 - Tutorials (attended or via remote access)
AF7 - Producing lab and work reports
AF8 - Individual study of subject contents
AF9 - Group work
A10 - Assessment tests
SE1 - Examination (written, oral and/or practical in the computer room)
SE2 - Assessment of written reports and work
SE3 - Assessment of oral work presentations
SE4 - Monitoring of attended activities
SE5 - Written Final Master's Dissertation report
SE6 - Defence of the Final Master's Dissertation
SE7 - Final report from the external tutor of the activity
Data Science, as it is known professionally, is an emerging field in science and technology that requires a multidisciplinary approach, normally combining broad knowledge from different thematic areas (Mathematics, Physics, IT).
In addition, this approach must take into account the importance of problems known as Big Data, which have had a huge impact in many areas both for research and industry, and which require a refocusing and redesign of corresponding general courses and education models. This is not a new concept and there are currently different courses at different levels in Spain and around the world that specifically look at the topic of Data Science.
Nonetheless, new components have emerged in recent years that have led to this Master in Data Science being proposed with a specific approach to the problems of Big Data:
The increasing interest and impact in many areas of Open Data that enable new challenges to be looked at whilst requiring considerable effort in comprehensive data management for integration and reuse. The rapid development of the field with the emergence of new standards and, particularly, the need to apply good practice in the ever more complex chain of data lifecycles require specific, advanced training. The RDA international forum (Research Data Allianz) is a good example of the efforts made along these lines. Its recommendations, developments and agreements are valuable training material for professionals.
The recent European Open Science Cloud initiative, launched in spring 2016 at the DG Research, attempts to connect results from the research world and the 'Digital Single Market' -an idea supported by both European institutions and all countries.
This Master's programme brings together the skills and strengths of the Universidad Internacional Menéndez Pelayo (UIMP) and the University of Cantabria (UC), formalised through a specific agreement to develop this and other official masters' programmes.
Moreover, this qualification benefits from the support provided by the agreement between the UIMP and the Spanish National Research Council (CSIC) for postgraduate teaching.
The partnership between the two universities and the CSIC in developing this master's programme, alongside coordination from the Cantabria Institute of Physics (IFCA, a joint CSIC-UC centre) and the Faculty of Sciences at the UC, enables access to highly specialised lecturers with clear visions of Open Science who can offer their scientifically focussed expertise: from knowledge on the data life cycle and its importance, to practical techniques in data acquisition, curation, processing, simulation, validation and preservation applied to large scientific projects and local initiatives.
Furthermore, students may access computing resources at the required scale to deal with real problems, including supercomputers, large storage and Cloud systems, as well as current solution development and application environments.
CG1 - Integrate effectively in a work group and work in a team, sharing available information and integrating their activity in the group activity, collaborating actively in the attainment of common objectives
CG2 - Capacity for study, synthesis and sufficient autonomy to develop basic research projects autonomously
CG3 - Draw up scientific and technical documents, particularly scientific articles
CG4 - Know how to prepare and conduct presentations, before a specialized public, about research or a scientific project
CG5 - Plan, design and launch an advanced project
CG6 - Search, obtain, process, communicate information and transform it into knowledge
CG7 - Know the methodological tools needed to develop advanced projects
CG8 - Ability to update knowledge exposed in the scientific community
CT1 - Analyse and combine information using different sources
CT2 - Know the ethical and legal problems related to data analysis and understand their importance for a society based on the values of freedom, justice, equality and pluralism
CT3 - The domain of time management
CT4 - Face up to tasks and critical situations
CT5 - Ability to work autonomously and decision-making
CT6 - Abilities associated with working in a team: cooperation, leadership, knowing how to listen
DSDA01 - Using predictive analysis to examine large volumes of data and discover new relationships
DSDA02 - Using suitable statistical techniques on available data to achieve an appropriate vision thereof
DSDA04 - Researching and analysing complex data sets, combining different data sources and types to improve global analysis
DSDA05 - Using different data analysis platforms to process complex data
DSDA06 – The ability to represent variable and complex data for visualisation
DSDM01 - Developing and implementing a data management strategy, specifically in the form of a data management plan (DMP)
DSDM02 - Developing and implementing data models, including metadata
DSDM03 - Collecting and integrating different data sources and their ingestion for subsequent analysis
DSDM05 - Ensuring data quality, accessibility and its publication format (curation)
DSDM06 - Managing IPR (Intellectual Property Rights) and ethical issues in data management
DSENG01 - Applying the principles of engineering to the research, design and development of a data analysis application prototype, or to the development of structures, instruments, machines, experiments, processes and systems required for it
DSENG02 - Developing and applying computing solutions for problems in a certain application domain, using a wide range of data analysis platforms
DSRM01 - Creating new visions and abilities through the use of scientific methods (hypothesis, testing and assessment) DSRM02 - Undertaking a systematic study aimed at more complete knowledge or the understanding of observable facts, and discovering new approaches to achieve aims in research and organisation
DSRM03 - Carrying out creative work, making systematic use of research or experimentation, to discover or review our knowledge of reality and using this knowledge in new applications
DSRM04 - The ability to convert strategies into action plans and carry them out until their conclusion
DSRM06 - Applying inherent acumen to resolve complex problems and develop innovative ideas
DSBPM01 - Understanding a research or business area and being able to translate unstructured problems into an abstract mathematic framework
DSBPM02 - Using available data to improve existing services or develop new services
DSBPM03 - Participating strategically and tactically, providing the view of Data Science in decisions that have an impact on administration and organisation
DSBPM04 - Providing scientific, technical and analytical support to other organisation sections
The Master's Degree in Data Science is especially aimed at graduates with 240 credits in Mathematics, Physics and Computer Engineering, who will be admitted without having to take supplementary training.
Since part of the teaching will be given in English, students must show accreditation of B2 level English. Failing to show official accreditation means students' knowledge may be verified in a test given by the Language Centre at the University of Cantabria.
Students from degree programmes with 240 credits in Telecommunications Engineering, Economics or other equivalent qualifications, as well as any other qualifications mentioned but with 180 credits -whether national or from overseas- may be admitted without supplementary training if they can accredit training worth at least 24 credits in mathematics (including statistics) and at least 12 credits in computing (including programming) as part of their university degree. Otherwise, they may be admitted subject to taking up to 30 credits of supplementary training.
The Master's programme has a professional orientation towards the business world. In addition to the content of the training, this approach is endorsed by the participation of professionals from different companies and the possibility of undertaking external placements.
Nevertheless, the training offered also enables an academic/research perspective that allows students to go on to undertake a PhD. This orientation is endorsed by the production of a Final Master's Dissertation with placements in different laboratories, incorporating students into one of the research groups of the lecturers taking part in the Master's programme.
Studies leading to this qualification will enable subsequent incorporation in R&D&i centres and teams, both public and private, including companies, to work on projects linked to Data Science technologies.
On this Master's Degree in Data Science you will:
Get to know and apply, with the supervision of experts from the CSIC, the University of Cantabria and specialised companies across Spain, the most up-to-date techniques in Data Science, and be able to propose a project that connects to your professional future.
Access the best data and computing resources in Europe.
Undertake placements at leading companies and research groups in areas such as Economics and Finance, the Internet of Things, Biomedicine, the Environment, Meteorology, Physics and Astronomy, Social Sciences, etc.
Apply for different financial aid to cover the cost of tuition.
Combine this Master's programme with your job, if necessary.
Start a career in research if you so desire, since it provides access to doctoral programmes.
Access with an official university degree from Spain or the European Higher Education Area (EHEA): Admission to the Master's Degree in Data Science requires an official university degree from Spain or another higher education institution belonging to another Member State of the EHEA which grants access to study a Master's Degree in the country of origin.
Access with a university degree from outside the EHEA: Students with degrees from education systems outside the EHEA can be accepted without requiring official recognition of their degrees by accrediting that the level of studies is the equivalent to that of official university degrees in Spain and that the degree allows access to postgraduate studies in the country of origin.
Access for these students depends on a favorable decision from the Rector. The decision by the Rector will never imply official recognition of the degree the student possesses nor its recognition for purposes other than to study the Master's Degree.
Applications for admission must be sent via the web site "On-line Pre-registration" which can be accessed from http://www.uimp.es/preins/index.php. When pre-registration is made, the required documentation must be attached in PDF format, although the documents do not need to be authenticated at the time of pre-registration. However, they should be certified because it will be essential to formalize registration, if admitted.
Since part of the teaching will be given in English, students must show accreditation of B2 level English. Failing to show official accreditation means students' knowledge may be verified in a test given by the Language Centre at the UC.
Graduates in Mathematics, Physics or Computer Engineering with 240 credits or more in university training may directly join the programme.
Graduates from overseas universities with these degrees will also have direct access without having to take supplementary training, as long as they are able to access postgraduate studies in their own country, and accredit having taken at least 24 credits in mathematics (including statistics) and 12 in computing (including programming).
Additionally, graduates from other Engineering programmes or Economics (with 240 credits or more), as long as they can show they have taken at least 24 credits in mathematics (including statistics) and 12 in computing (including programming).
Students with the qualifications set out in ii) and iii) may be admitted with supplementary training of up to 30 credits if they do not comply with the aforementioned educational requirements.
Photocopy of National ID card, in the case of Spanish citizens, or of passport or identity document, in the case of foreign nationals.
Photocopy of the Qualification Certificate which provides access to Masters' Degree studies, or proof of having paid for the issuance of the certificate concerned.
Certification of personal academic records.
ID or Passport-size photograph, in JPG format.
Curriculum Vitae, in PDF format, so as to facilitate the assessment of other merits appropriate to the admissions profile.
Students presenting foreign and non-homologated degrees must also provide to access:
Certification from the issuing university stating that the degree offered qualifies one for access to postgraduate studies in the country in question.
Personal academic certification which specifies the study program completed; official duration of the program in academic years and listing of courses taken, grade, and credit load of each one.
IMPORTANT: The academic documents of foreign degrees are to be presented translated into Spanish, where necessary; all those corresponding to countries not belonging to the EHEA are to be certified (on the UIMP website, general information on these requirements can be found).
The required documentation in original format should ONLY be submitted to the Student Administration Office (Secretaría de Estudiantes. C/ Isaac Peral 23. 28040 Madrid , Spain) in the event that the application has been passed by the Academic Committee for the Master's.
Universal accessibility will be guaranteed and the necessary resources and support will be monitored for those students with disabilities in order to assure the correct completion of the Master's. We ask that these students indicate their specific needs when enrolling.
In this link you can consult the UIMP Protocol for the attention of students with specific educational needs.
Where the number of applications exceeds the maximum number of new student places, candidates will be admitted in line with the following assessment criteria:
a) Academic transcript.
b) In the event of doubts or ties, the candidate will undergo a personal interview.
The Academic Commission may admit students and assign them up to 30 credits in supplementary training. Therefore, students who fail to accredit sufficient training in mathematics and/or computing shall undertake supplementary training with up to 18 credits in mathematics and 12 in computing.
In exceptional cases, where the required supplementary training does not represent an excessive additional load for students, the Academic Commission may authorise it be taken at the same time as the Master's programme. This process is always done by assigning a Tutor (a Master's lecturer) who will undertake personal monitoring of students to ensure their success in the supplementary training to be taken.
The Commission will publish the admissions list every academic year on the UIMP website.
Teaching up to 30 credits of supplementary training has been planned in first- and second-year subjects on the degrees in Mathematics, Computer Engineering and Physics at the UC to supplement the training of students who require it.
Supplementary training includes two fields that are, in turn, spread over subjects in the following way:
Mathematics (18 credits)
Algebra: 6 credits
Probability and Statistics: 6 credits
Mathematical Analysis: 6 credits
Computing (12 credits)
Programming: 6 credits
Information Systems: 6 credits
Once registered, and regardless of the institution where they take the course (UIMP or UC), students will receive support and guidance via different sources:
The UIMP and UC Faculty of Sciences websites that will include general academic information as well as specific information on this Master's programme.
Personal tutoring: advice in academic matters.
A virtual interactive platform where students will receive all programmes, presentations, notes, recordings from classes (where possible), practicals, activity schedule and necessary material for taking the different subjects. In turn, this platform is the tool where students will produce or submit much of their practical work to lecturers, including tests, problem solving, etc.
UC Library: courses on finding information.
SOUCAN: techniques and guidance for studying, oral communication and intelligence.
The Master's Academic Commission will advise students on all academic aspects that cannot be directly resolved by lecturers. When doing external placements, data labs and the Final Master's Dissertation, students will have an assigned lecturer who will be their tutor or supervisor and be responsible for monitoring, supervising and advising on their work throughout the duration of the course.
Francisco Matorras Weinig. Department Chair of Atomic, Molecular and Nuclear Physics, University of Cantabria (UC)
Lara Lloret Iglesias. Postdoctoral Researcher, Cantabria Institute of Physics (IFCA), CSIC-UC
Coordinator
Francisco Matorras Weinig. Department Chair of Atomic, Molecular and
Nuclear Physics, University of Cantabria (UC)
Coordinator
Lara Lloret Iglesias. Postdoctoral Researcher, Cantabria Institute
of Physics (IFCA), CSIC-UC
Vocals
Fernando
Aguilar Gómez. PhD Science and Technology, Cantabria Institute of
Physics (IFCA), CSIC-UC
Rodrigo
García Manzanas,. Associate Professor, University of Cantabria (UC)
Diego García Saiz. Assistant Professor, University of
Cantabria (UC)
Steven
Van Varenbergh,
Contact email: info-masterdatascience@listas.csic.es
The academic staff on the Master's programme includes lecturers from the University of Cantabria (UC) and researchers from the Spanish National Research Council (CSIC), all of whom have wide teaching and research experience and expertise in topics related to Data Science.
They will be joined by national and international experts from different research centres and areas who will provide their own experience and vision. There will also be many professionals from businesses in the field who will also pass on their own vision that is closer to commercial application.
Furthermore, the course has all resources available to researchers in the European Open Science Cloud: from large data servers to supercomputers, resources which are normally only within the reach of a few, global top-flight professionals.
The detailed list of lecturers on the programme can be viewed in the 'Syllabus Guide' section of this website. Each subject has a coordinator to ensure the teaching programme is followed.
Fernando Aguilar Gómez. Doctor of Science and Technology, Cantabria Institute of Physics (IFCA), CSIC-UC
Alicia Calderón Tazón. Doctor Researcher Ramón y Cajal, Cantabria Institute of Physics (IFCA), CSIC-UC
Antonio Santiago Cofiño González. Associate Professor, University of Cantabria (UC)
Marcos Cruz Rodríguez. Professor of Statistics and Operational Research, University of Cantabria (UC)
Pablo María de Castro García. Assistant Professor, Universidad de Cantabria (UC), Managing Partner of Conceptual KLT, S.L.
José Manuel Gutiérrez Llorente. Professor, Cantabria Institute of Physics (IFCA), CSIC-UC
Sixto Herrera García. Assistant Professor of Applied Mathematics, University of Cantabria (UC)
Álvaro López García. Postdoctoral Researcher, Cantabria Institute of Physics (IFCA), CSIC-UC
Lara Lloret Iglesias. Postdoctoral Researcher, Cantabria Institute of Physics (IFCA), CSIC-UC
Francisco Matorras Weinig. Department Chair of Atomic, Molecular and Nuclear Physics, University of Cantabria (UC)
Pablo Orviz Fernández. Researcher, Cantabria Institute of Physics (IFCA), CSIC-UC
Luis Ignacio Santamaría Caballero. Department Chair of Signal Theory and Communications, University of Cantabria (UC)
Cristina Tirnauca. Assistant Professor, Universidad de Cantabria (UC)
Begoña Torre Olmo. Professor of Financial Economics and Accounting, University of Cantabria (UC)
Ignacio Alejandro Varela Egocheaga. Doctor, University of Cantabria (UC)