Bachelor in Data Science and Engineering

• Program modified in 2025. In 2025/26 will be offered 1st year.

• Field of knowledge: Electrical engineering, electronic engineering, and telecommunications engineering.

• To find out the final list of electives available for enrolment and their corresponding semester, please consult the following link in the Secretaría Virtual.

Study plan for students who started in the academic year 2024/25 or earlier.More information in Aula Global.

• In 2025/26 only 2nd, 3rd and 4th year will be offered

• To find out the final list of electives available for enrolment and their corresponding semester, please consult the following link in the Secretaría Virtual.

Exchange programs

The Erasmus programme permits UC3M first degree and post graduate students to spend one or several terms at one of the European universities with which UC3M has special agreements or take up an Erasmus Placement, that is a work placement or internship at an EU company. These exchanges are funded with Erasmus Grants which are provided by the EU and the Spanish Ministry of Education.

The non-european mobility program enables UC3M degree students to study one or several terms in one of the international universities with which the university has special agreements. It also has funding from the Banco Santander and the UC3M.

These places are offered in a public competition and are awarded to students with the best academic record and who have passed the language threshold  (English, French, German etc..) requested by the university of destination.

Movilidad europea

Movilidad no europea

Entry profile

In view of the access routes and requirements, it is highly recommended that students entering this Degree have studied the Baccalaureate in Science (or, where appropriate, an equivalent Baccalaureate or similar in terms of the subjects studied when the student comes from non-Spanish educational systems).
As can be seen in the Programme, this degree combines the learning of a set of multidisciplinary knowledge and competences from areas of knowledge such as mathematics, statistics, computer science and telecommunications.
In relation to access to Vocational Training, although there are no access limitations to the degrees depending on the branch to which they are attached, for access to this degree it is more recommendable to take the Higher Level Training Cycles of the professional family of Computer Science and Communications, especially the training cycles of Higher Technician in Administration of Networked Computer Systems, development of multiplatform applications and development of web applications.
If we are to highlight any suitable competence content in relation to the entry profile, the student should have a good previous training in Mathematics. Personal attitudes of initiative, teamwork, personal organisation of work, capacity for abstraction, critical thinking and responsibility and interest in the practical application of knowledge to solve real problems are highly valued, as well as a high level of competence in management skills and technological management.
Finally, the University only offers the degree in English, which means that students must complete their 240 credits in English. Therefore, students must demonstrate a good level of linguistic competence in English equivalent to level B2 in the Common European Framework of Reference for Languages, given that they will be taught in English and will be working with texts, materials, exercises, etc. all in English.

Application for a place in the degree

Graduate profile

Graduates of the Bachelor's Degree in Data Science and Engineering must be able to design and manage infrastructures that support large amounts of data for subsequent analysis, to design and build systems capable of integrating data from various sources and process large volumes of data in order to optimise the performance of the data ecosystem of a company, organisation or entity. In addition, graduates will be able to convert raw data into knowledge, applying statistical, machine learning and pattern recognition techniques to solve critical business problems.

To this end, graduates will have strong programming skills, the ability to design new algorithms, handle large volumes of data and the analytical skills to interpret the results of their findings and display them using visualisation techniques. Graduates will also need to be up to date with the latest cutting-edge computing technologies, as they will have to work with datasets of different nature and be able to run their algorithms on big data effectively and efficiently.

Furthermore, they will be able to develop their professional career in all industrial and professional sectors that demand the profile of a data scientist and data engineer.

The work of the data scientist is closely related to business strategy in a wide variety of sectors, as machine learning and artificial intelligence technologies find application at various business levels, ranging from business intelligence itself to human resources, to customer and supplier management or digital marketing.

In particular, we highlight certain strategic sectors in which artificial intelligence is expected to have a strong impact: high technology and communications, media and entertainment, automotive and assembly, resources and utilities, transportation and logistics, healthcare, biosciences, professional services, retail, education, marketing, customer and supplier relations, and the public sector.

1. Knowledge of Titles

K1 - To know the principles and values of democracy and sustainable development, in particular, respect for human rights and fundamental rights, gender equality and non-discrimination, the principles of universal accessibility and climate change, in line with their professional development in the field of the degree.

K2 - To know basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile.

K3 - To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study.

K4 - Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations, in the field of data storage, management and processing.

K5 - Ability to understand and relate fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them.

K6 - Acquire the fundamentals of Bayesian Statistics and learn the different techniques of intensive computing to implement Bayesian inference and prediction, applying them to data analysis, uncertainty modeling, and decision-making in real-world problems in Data Science and Engineering.

K7 - Assimilate basic concepts of programming, including control structures, data types, and functions, and their application in developing programs for data analysis, processing, and visualization in the field of Data Science and Engineering.

K8 - Differentiate data structures, algorithms, databases and files oriented to data processing.

K9 - To know the theory of languages, grammars and automata and their application to lexical and syntactic analysis associated with data analysis.

K10 - To know and manage the fundamentals of analog and digital signal processing in the time and frequency domains, including sampling, filtering and transforms, with applications to signal processing in the field of Data Science and Engineering.

K11 - Understand the modeling, prediction, filtering and smoothing of random signals and stochastic processes, with applications in time series analysis, pattern detection, and optimization of models in Data Science and Engineering.

K12 - Know and understand the fundamentals of network architectures, security requirements (with an emphasis on privacy) of big data environments and the consequent protection measures: technical; organizational and legal, as well as to know and handle encryption techniques and their use to guarantee data security.

K13 - To know and identify basic and current aspects of the functional areas of the company and understand the relationship between them to promote entrepreneurship, in the development and implementation of systems in the field of data science and engineering.

2. Skills of Titles

S1 - To plan and organize team work making the right decisions based on available information and gathering data in digital environments.

S2 - To use information interpreting relevant data avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to assess the reliability and quality of such information.

S3 - Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science, applying knowledge of mathematics, probability and statistics, programming, databases, and languages, grammars and automata.

S4 - Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques, and applying knowledge of: algebra; geometry; differential and integral calculus; numerical methods; numerical algorithms; statistics and optimization.

S5 - Ability to correctly identify predictive problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of regression analysis as the basis for prediction methods.

S6 - Ability to correctly identify classification problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of multivariate analysis as the basis for classification, clustering and dimension reduction methods.

S7 - Capability for mathematical modeling, algorithmic implementation and optimization problem solving related to data science, relying on knowledge of mathematics, algorithms, programming and optimization.

S8 - Ability to use the main technologies used for processing large amounts of data, taking into account the knowledge of security and protection measures in these environments.

S9 - Apply, design, develop, critically analyze and evaluate machine learning methods in classification, regression and clustering problems and for supervised, unsupervised and reinforcement learning tasks.

S10 - Apply, design, develop, critically analyze and evaluate solutions based on artificial neural networks.

S11 - Apply, design, develop, critically analyze and evaluate solutions based on machine learning for applications in specific domains such as recommendation systems, natural language processing, Web or social networks.

S12 - Apply, design, develop, critically analyze and evaluate image and video processing, and computer vision solutions.

S13 - Apply fundamental knowledge of network architectures.

S14 - Apply, design and develop Web and applications and use them to capture data.

S15 - Apply, combine,design and develop data visualization tools to communicate the results of data analysis, adapting them to different audiences, both technical and non-technical.

S16 - Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences.

3. Competences of Titles

C1 - To Know and be able to handle interpersonal skills on initiative, responsibility, conflict resolution, negotiation, etc., required in the professional environment.

C2 - To develop those learning skills necessary to undertake further studies with a high degree of autonomy.

C3 - Ability to solve problems with initiative, decision making, creativity, and to communicate and transmit knowledge, skills and abilities, understanding the ethical, social and professional responsibility of the data processing activity. Leadership capacity, innovation and entrepreneurial spirit.

C4 - Teamwork in international and interdisciplinary contexts.

C5 - Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.

C6 - To present and defend, individually and before a university panel a project in the area of the specific technologies of Data Science and Engineering, being of a professional nature, which synthesizes and encompasses the competences acquired in the degree program.

C7 - To apply and adapt technical knowledge and practical skills in the field of Data Science and Engineering, participating in problem-solving and the development of solutions in a professional environment.

External internships

This is a selection of places where students of this degree can do their internships:

• ACCENTURE, S.L., SOC UNIPERSONAL
• ÁLAMOCONSULTING, S.L.
• BANCO BILBAO VIZCAYA ARGENTARIA, S.A.
• BANKINTER, S.A.
• CASE ON IT
• DECIDE SOLUCIONES, S.L.
• DEVOTEAM DRAGO S.A.U.
• ESELEC INGENIEROS, S.L.
• FINTONIC SERVICIOS FINANCIEROS, S.L
• FUNDACIÓN UNIVERSIDAD EMPRESA
• HAVAS MEDIA GROUP SPAIN S.A.U
• Holcim EMEA Digital Center S.L.U
• ING BANK NV SUCURSAL EN ESPAÑA
• INNOVACIÓN TECNOLÓGICA Y SOLUCIONES DE NEGOCIO, S.L.
• JOHN DEERE IBÉRICA, S.A.
• Jungheinrich Digital Solutions SL
• KPMG ASESORES S.L
• LECA Solutions
• NFOQUE ADVISORY SERVICES,S.L
• NTT DATA SPAIN, S.L.U.
• OLIVER WYMAN, S.L.
• PIXELABS
• PRICEWATERHOUSECOOPERS ASESORES DE NEGOCIOS, S.L.
• SAS INSTITUTE, S.A.
• SDG CONSULTING ESPAÑA SAU
• SOCIEDAD ESPAÑOLA DE SISTEMAS DE PAGO, S.A.
• SOCIEDAD ESTATAL DE CORREOS Y TELEGRAFOS, S.A., S.M.E
• TECNILÓGICA ECOSISTEMAS, S.A.U.
• TomTom Sales Branch Spain
• VASS CONSULTORÍA DE SISTEMAS, S. L.

Salidas profesionales

The work of the data scientist is closely related to business strategy in a wide variety of sectors, as machine learning and artificial intelligence technologies find application at very different levels, ranging from business intelligence itself, to human resources selection, to customer and supplier management or digital marketing. 
 
In particular, we highlight certain strategic sectors in which artificial intelligence is expected to have a strong impact: high technology and communications, media and entertainment, automotive and assembly, basic resources and services, transportation and logistics, healthcare, biosciences, professional services, retail, education, marketing, customer and supplier relations, and the public sector.
 

As a consequence, there is a wide range of job possibilities for the data scientist and data engineer, among which we can mention, for example:

• Data Scientist (general denomination that encompasses data management, design and development of artificial intelligence algorithms in any sector).
• Data Engineer (general denomination that gives hardware and software support to Data Science in any sector)
• Software Developer (software engineering in the field of Artificial Intelligence)
• Web/mobile application developer (data capture, storage, and management)
• Intelligent services designer and developer
• Strategy Engineer (alignment of the organization's strategy with the required technology)
• Data Analytics Manager
• Digital Business Leader & Strategist
• Digital Business Development Manager
• Digital Innovation Manager, Digital Product
• Service Designer and Developer
• Digital Marketing Manager
• Digital Research & Development Manager
• Digital Business Consultant
• Chief Executive Officer
• Chief Digital Transformation Officer
• Chief Digital Marketing Officer
• Chief Digital Sales Officer
• Chief Digital Operations Officer
• Digital Transformation Manager

Schedule and calendars

Schedule in bachelor's degree

Academic calendar

Exams calendar (first semester)

Exams calendar (second semester)

Exams calendar (recovery tests)

Schedule and calendars

Schedule in bachelor's degree

Academic calendar

Exams calendar (first semester)

Exams calendar (second semester)

Exams calendar (recovery tests)