Bachelor’s Degree in Mathematical Engineering in Data Science

Program Overview

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Bachelor’s Degree in Mathematical Engineering in Data Science

Overview

The Bachelor’s Degree in Mathematical Engineering in Data Science is a four-year program in English designed to train the mathematicians of the future with a global vision of computing, calculus, and business as data scientists.

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

• Top facilities: Access to advanced laboratories and simulation rooms.
• A unique study plan: Develop knowledge in mathematics, computing, and business development.
• Get specialized to set yourself apart from the rest: Prepare for the Cisco CCNA and Amazon Web Services Cloud Architecture certificate.
• Be in contact with companies: Carry out internships in companies like European Space Agency, Iberia, Altran, Aernnova, Airbus, Siemens, IBM, Telefónica, Vodafone, Deloitte, Everis, Indra Systems, Bosch Security Systems.

Study Plan

• Mathematics, specialized in the branch of statistics.
• Computing focused on big data.
• Business development and analysis for companies.

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 tools 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 decision-making 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.
• 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 professional activities, as well as sensitivity to inequality and diversity.

Employability

Career Opportunities

• Big data.
• Engineering or energy companies.
• Robotic computer and technology companies.
• Transport and automotive.
• Artificial intelligence.
• Insurance, banking, finance, and capital risk companies.

Mathematical Techniques

• 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.

Management and Consultancy

• Data consultant.
• Big data systems auditor.
• Managerial career.

Start-ups

• Technology start-ups.

R&D&I and Teaching

• Teaching in the public and private sector.
• R&D&I departments.
• Other research by public bodies.

Admissions

Admission Exams

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

Place Reservation

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

Enrollment

Submit the required documents to formalize your enrollment.

Scholarships and Financial Aid

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.

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.

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.

Internal Quality Assurance System (IQAS)

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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

Evaluation Process Results and RUCT

Process Results

• 2017 Verification Report
• Modification Report 2018
• Modification Report 2019
• Special Follow-Up Report 2019
• 2020 Follow-Up Report
• Modification Report 2021
• Modification Report 2024

Link to RUCT

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Regulations

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Student Services

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