16958

What are you looking for?

Ej: Medical degree, admissions, grants...

Admissions:+34 917407272
Students:+34 914146611
Whatsapp

Bachelor’s Degree in Mathematical Engineering in Data Science* Madrid

First private university to commit to science degrees with the launch of Mathematical Engineering applied to data analysis at our Madrid campus.

Select your program

-50% ¡Descuento en reserva de plaza hasta el 30 de noviembre!

Mathematical Engineering degree

The degree in Mathematical Engineering at Universidad Europea is a four-year programme in English designed to train the mathematicians of the future with a global vision of computing, calculus and business as data scientist.

Mathematical engineering includes a variety of areas and specialisations, and our curriculum covers many areas that you can deepen your interest in over time, including operation, development, design and administration of mass data systems.

Our academic model is based on experiential learning. As well as benefitting from being able to study in state-of-the-art facilities, you will also take part in internships and work placements at leading companies in different sectors such as healthcare, energy, education, industry and research.

Official degree issued by Universidad Europea de Madrid
Campus-based
Villaviciosa de Odón 4 Years, 240 ECTS
Start: September 2025
School of Architecture, Engineering and Design - Madrid

Why study the Bachelor’s Degree in Mathematical Engineering in Data Science?

Top facilities

You will have access to the most advanced laboratories and simulation rooms so you can get the most out of our academic model:

  • The Tech Factory to develop your software applications and classroom or personal projects, equipped with software with professional licenses to help you grow as a computer expert.
  • The Robot Learning Lab has a Pepper robot, NAO robots, Qbots and tools for designing your own robots.
  • The Industry 4.0 Lab for learning how to use IoT industrial cybersecurity technologies.
  • Plus, the Fab Lab to create your prototypes, the Electronics Lab, the CISCO Networking Lab, the Advanced Computer Centre, the Computer Club and the CRAI Dulce Chacón Library and University Residence.
A unique study plan

Studying Mathematics leads you to a better understanding of what is going on around you. You will learn to analyse, interpret and solve problems using logic and mathematical models. You will be able to address functions, such as the operation, development, design and administration of mass data systems and analytical functions to apply your knowledge to multiple industries: healthcare, energy, education, industry and research.

You will develop knowledge over 3 areas:

  • Mathematics, specialized in the branch of statistics.
  • Computing focused on big data.
  • Business development and analysis for companies.
Get specialized to set yourself apart from the rest

You will prepare for the Cisco CCNA and Amazon Web Services Cloud Architecture certificate and work in the Network Lab, Robotics Lab with Scorbot and Fanuc robots, and practice with the Lego Mindstorm System, and the Electronics, Analogue and Digital Lab.

Be in contact with companies

Carry out internships in companies: European Space Agency, Iberia, Altran, Aernnova, Airbus, Siemens, IBM, Telefónica, Vodafone, Deloitte, Everis, Indra Systems, Bosch Security Systems.

The present and the future are being written in mathematical language

Mathematics has always been indispensable; today there is no corner of the industry or society that it does not reach. Science and technology continue to revolutionize the way in which we understand and solve problems, and the key to all this is in mathematics.

Through the experience of great professionals, discover what the careers of the future will be, their application in different sectors, trends and opportunities that are yet to come.

Are you interested to discover why the future depends on mathematics?

Study plan

Studying Mathematics leads you to a better understanding of what is going on around you. You will learn to analyse, interpret and solve problems using logic and mathematical models. You will be able to address functions, such as the operation, development, design and administration of mass data systems and analytical functions to apply your knowledge to multiple industries: healthcare, energy, education, industry and research.

120.

Competencies of the degree

Knowledge
  • CON01 - Describe the concept of a company, as well as its functional areas, including the relationships between them, and the application of the various available tools in each of them necessary for performance as a data scientist.
  • CON02 - Describe the fundamentals, principles, and applications of computer systems, software development, and databases.
  • CON03 - Explain the fundamental principles and basic techniques of intelligent systems and their practical application.
  • CON04 - Explain the Data Lifecycle, from operation to visualization. From data to knowledge and from knowledge to business strategy.
  • CON05 - Describe the models and forms of service evaluation based on criteria of utilization capacity and service quality in the field of data science application.
  • CON06 - State the legislation regarding personal data, privacy, and fundamental rights of individuals, as well as the current evaluation and certification criteria and mechanisms for security, while assessing and ensuring policies and actions of high ethical rigor and social responsibility.
  • CON07 - Describe techniques for system interoperability and data integration and aggregation.
  • CON08 - Acquire knowledge of linear algebra, differential and integral calculus, numerical methods, statistics, and optimization for problem-solving.
  • CON09 - Describe the concepts of descriptive statistics and probabilistic models, and provide examples of their application in real contexts, explaining the difference between descriptive and inferential studies.
  • CON10 - Interpret the meaning of the derivative and the integral, explain the concept of differential and Taylor polynomial, and apply these principles to solve optimization problems and calculate integrals.
  • CON11 - Explain the concepts of linear systems, matrices, eigenvalues, and eigenvectors, and diagonalization, and illustrate how to solve linear systems and how to calculate an eigenvalue and eigenvector.
Competency
  • CP01 - Actively participate in projects involving the use of open data and statistical analysis tools in distributed environments
  • CP02 - Actively participate in projects within the realm of large-scale data systems that require knowledge, evaluation, selection, and utilization of support tolos for big data project development
  • CP03 - Execute, present, and defend an original individual exercise, and present and defend before a university tribunal, consisting of a project in the field of data science of a professional nature synthesizing and integrating the competencies acquired in the teachings
  • CP04 - Apply machine learning techniques to design and implement applications and systems that utilize them, including those dedicated to automatic extraction of information and knowledge from large volumes of data, considering possible data quality issues.
  • CP05 - Apply mathematical, statistical, and optimization techniques and models to data processing, decision support systems, exploration of relationships between variables, and making predictions
  • CP06 - Extract information from structured, semi-structured, or unstructured data, including text, image, video, and audio, using techniques for identification and acquisition of relevant data, reduction, compression, transformation, cleaning, and evaluation of its quality
  • CP07 - Represent knowledge with formalisms based on logic and perform inference to derive new knowledge, as well as its use for metadata management and governance of complex data systems.
  • CP08 - Apply methodologies, architectures, and techniques specific to Big Data for storage and management of large volumes of data, efficient management of continuous data streams, and application of data integration methods.
  • CP09 - Apply models and standards in the field of large data systems
  • CP10 - Apply statistical thinking and can tackle the different stages of a statistical study (from problem formulation to result exposition).
  • CP11 - Utilize information and communication technologies for data search and analysis, communication, and learning.
  • CP12 - Integrate analysis with critical thinking in an evaluation process of different ideas or professional possibilities and their potential for error, based on evidence and objective data leading to effective and valid decisionmaking in the field of data science.
  • CP13 - Apply techniques for designing, implementing, capturing, storing, and exploiting databases and database management systems, both structured and unstructured, monolithic and distributed.
  • CP14 - Design efficient interfaces in the context of Big Data that ensure accessibility and usability, using techniques for graphical and analytical representation.
Skills
  • HAB01 - Apply data type models and algorithms efficiently to devise solutions for problems.
  • HAB02 - Use mathematical language and its application to state propositions and convey acquired knowledge in various fields of mathematics.
  • HAB03 - Use computer tools for statistical analysis, numerical and symbolic calculation, graphical visualization, optimization, and others to experiment in Mathematics and solve problems.
  • HAB04 - Evaluate trends in the digital Economy market, as well as their impact on social, economic, and cultural development.
  • HAB05 - Analyze digital marketing techniques by assessing the impact of decisions on profits, the market, people, and society.
  • HAB06 - Analyze information systems engineering techniques in business processes.
  • HAB07 - Analyze data replication, conservation, restoration, and anonymization techniques.
  • HAB08 - Generate new ideas and concepts from known ideas and concepts, reaching conclusions or solving problems, challenges, and situations in an original way in the academic and professional environment.
  • HAB09 - Transmit messages (ideas, concepts, feelings, arguments), both orally and in writing, strategically aligning the interests of the various stakeholders involved in communication in the academic and professional environment. TIPO: Habilidades o destrezas
  • HAB10 - Influence others to guide them towards specific objectives and goals, taking into consideration their viewpoints, especially in professional situations arising from the volatile, uncertain, complex, and ambiguous (VUCA) environments of the current world.
  • HAB11 - Cooperate with others in achieving a shared academic or professional objective, participating actively, empathetically, and exercising active listening and respect for all participants.
  • HAB12 - Adapt to adverse, unexpected situations that cause stress, whether personal or professional, overcoming them and even turning them into opportunities for positive change.
  • HAB13 - Demonstrate ethical behavior and social commitment in carrying out profesional activities, as well as sensitivity to inequality and diversity.

Employability

Career Opportunities

The main career opportunities for graduates of Bachelor’s Degree in Mathematical Engineering in Data Science are:

  • Big data.
  • Engineering or energy companies.
  • Robotic computer and technology companies.
  • Transport and automotive.
  • Artificial intelligence.
  • Insurance, banking, finance and capital risk companies.
  • Data scientist (data analysis).
  • Data engineer.
  • Big data systems administrator/ or developer.
  • Big data infrastructure manager.
  • Head of safety/privacy in big data projects.
  • Big data/business intelligence solutions architect.
  • Data consultant.
  • Big data systems auditor.
  • Managerial career.
  • Technology start-ups.
  • Teaching in the public and private sector.
  • R&D&I departments
  • Other research by public bodies

Admissions

Start your future at Universidad Europea

You can become a student at Universidad Europea in three easy steps.

1

Admission exams

Start your admission process by calling +34 917407272 or request information and our advisors will contact you.

2

Place reservation

Once you have been admitted, secure your place by paying the reservation fee.

3

Enrollment

Submit the required documents to formalise your enrollment.

Scholarships and financial aid

girl student

We want to help you. If you want to study at the Universidad Europea, you will have at your disposal a wide selection of own and official scholarships.

girl student

Credit recognition and transfers

You don’t have to stick with something you don’t like. That’s why we’ve designed specific plans for credit recognition and transfers. Request your online credit recognition review, transfer your academic file and start studying at Universidad Europea.

Your virtual tour begins here!

HPR Lab Universidad Europea de Madrid

Experience first-hand what it is like to study at Universidad Europea: our facilities and our experiential learning model.

HPR Lab Universidad Europea de Madrid
Visita el campus Universidad Europea

Come and see the campus

Get to know the facilities and discover why Universidad Europea is made for you.

Academic quality

As part of its strategy, the University has an internal quality plan whose objective is to promote a culture of quality and continuous improvement, and which allows it to face future challenges with the maximum guarantee of success. In this way, it is committed to promoting the achievement of external recognitions and accreditations, both nationally and internationally; the measurement and analysis of results; simplification in management; and the relationship with the external regulator.

View

Internal Quality Assurance System (IQAS)
Monitoring the quality of the degree

Members of the Quality Commission of the Degree (CCT)

  • Vice-Dean of Undergraduate Studies
  • Degree Coordinator
  • Department Director
  • Students
  • Faculty (TFG Coordinator and Internship Coordinator)
  • Quality Manager (Quality and Academic Compliance)
  • Academic Advisor
  • Academic Director
  • Evaluation and Learning Manager

Main results of the degree

  • DROPOUT RATE: 5.9%.
  • EFFICIENCY RATE: 97.6%.
  • GRADUATION RATE: 47.4%.
  • EMPLOYABILITY RATE: 100%.
  • STUDENT SATISFACTION WITH THE DEGREE: 4.0/5
  • SATISFACTION OF PROFESSORS WITH THE DEGREE: 3.8/5
  • STUDENT SATISFACTION WITH THE FACULTY: 4.2/5
  • PAS SATISFACTION WITH FACULTY/SCHOOL: 4.0/5
  • SATISFACTION OF GRADUATES WITH THE DEGREE PROGRAM: 4.7/5
Regulations
None