Bachelor in Data Science and Engineering

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.

BASIC SKILLS:          

CB1        Que los estudiantes hayan demostrado poseer y comprender conocimientos en un área de estudio que parte de la base de la educación secundaria general, y se suele encontrar a unnivel que, si bien se apoya en libros de texto avanzados, incluye también algunos aspectos que implican conocimientos procedentes de la vanguardia de su campo de estudio.

CB2        Que los estudiantes sepan aplicar sus conocimientos a su trabajo o vocación de una forma profesional y posean las competencias que suelen demostrarse por medio de la elaboración y defensa de argumentos y la resolución de problemas dentro de su área de estudio

CB3        Que los estudiantes tengan la capacidad de reunir e interpretar datos relevantes (normalmente dentro de su área de estudio) para emitir juicios que incluyan una reflexión sobre temas relevantes de índole social, científica o ética

CB4        Que los estudiantes puedan transmitir información, ideas, problemas y soluciones a un público tanto especializado como no especializado.

CB5        Que los estudiantes hayan desarrollado aquellas habilidades de aprendizaje necesarias para emprender estudios posteriores con un alto grado de autonomía.

GENERAL SKILLS:            

CG1       Adequate knowledge and skills to analyze and synthesize basic problems related to engineering and data science, solve them and communicate them efficiently.

CG2       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.

CG3       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.

CG4       Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science.

CG5       Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques.

CG6       Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally

               

TRANSVERSAL SKILLS:  

CT1        Ability to communicate knowledge orally and in writing to both specialised and non-specialised audiences

CT2        Teamwork in international and interdisciplinary contexts

CT3        To acquire basic humanistic knowledge that allows to complete the transversal formative profile of the student

CT4        To know and be able to handle interpersonal skills about initiative and responsibility, negotiation, emotional intelligence, etc. as well as calculation tools that allow to consolidate the basic technical skills that are required in any professional environment

SPECIFIC SKILLS:

CE1        Ability to solve mathematical problems that may arise in data engineering and science. Ability to apply knowledge of: algebra; geometry; differential and integral calculus; numerical methods; numerical algorithms; statistics and optimization

CE2        Ability to correctly identify predictive problems corresponding to certain objectives and data and to use the basic results of regression analysis as the basis for prediction methods

CE3        Ability to correctly identify classification problems corresponding to certain objectives and data and to use the basic results of multivariate analysis as the basis for classification, clustering and dimension reduction methods

CE4        Capability for mathematical modeling, algorithmic implementation and optimization problem solving related to data science

CE5        Ability to understand and manage fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them

CE6        Ability to acquire the fundamentals of Bayesian Statistics and learn the different techniques of intensive computing to implement Bayesian inference and prediction

CE7        Ability to assimilate basic concepts of programming and ability to perform programs oriented to data analysis.

CE8        Ability to differentiate data structures, algorithms, databases and files oriented to data processing

CE9        Ability to know the theory of languages, grammars and automata and their application to lexical and syntactic analysis associated with data analysis.

CE10      Ability to use the main technologies used for processing large amounts of data

CE11      Ability to analyze and process analog and digital signals in the time and frequency domains

CE12      Ability to model, predict, filter, and smooth random signals and stochastic processes

CE13      Ability to apply and design machine learning methods in classification, regression and clustering problems and for supervised, unsupervised and reinforcement learning tasks

CE14      Ability to design solutions based on artificial neural networks

CE15      Ability to design solutions based on machine learning for applications in specific domains such as recommendation systems, natural language processing, Web or social networks

CE16      Ability to design audio and video processing, and computer vision solutions

CE17      Ability to know the 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

CE18      Ability to acquire basic and fundamental knowledge of network architectures

CE19      Ability to develop Web and mobile applications and use them to capture data with them

CE20      Ability to use data visualization tools to communicate the results of data analysis, adapting them to different audiences, both technical and non-technical

CE21      Ability to use modern optimization tools to solve practical problems efficiently

CE22      Ability to identify basic and current aspects of the functional areas of the company and understand the relationship between them to promote entrepreneurship

CE23      To know how to analyze, elaborate and defend individually a problem and its solution within the disciplinary scope of the Degree, applying the knowledge, skills, tools and strategies acquired or developed in it

RA1 Haber adquirido conocimientos avanzados y demostrado una comprensión de los aspectos teóricos y prácticos y de la metodología de trabajo en el campo de la ciencias e ingeniería de datos con una profundidad que llegue hasta la vanguardia del conocimiento

RA2 Poder, mediante argumentos o procedimientos elaborados y sustentados por ellos mismos, aplicar sus conocimientos, la comprensión de estos y sus capacidades de resolución de problemas en ámbitos laborales complejos o profesionales y especializados que requieren el uso de ideas creativas e innovadoras

RA3 Tener la capacidad de recopilar e interpretar datos e informaciones sobre las que fundamentar sus conclusiones incluyendo, cuando sea preciso y pertinente, la reflexión sobre asuntos de índole social, científica o ética en el ámbito de su campo de estudio;

RA4 Ser capaces de desenvolverse en situaciones complejas o que requieran el desarrollo de nuevas soluciones tanto en el ámbito académico como laboral o profesional dentro de su campo de estudio;

RA5 Saber comunicar a todo tipo de audiencias (especializadas o no) de manera clara y precisa, conocimientos, metodologías, ideas, problemas y soluciones en el ámbito de su campo de estudio;

RA6 Ser capaces de identificar sus propias necesidades formativas en su campo de estudio y entorno laboral o profesional y de organizar su propio aprendizaje con un alto grado de autonomía en todo tipo de contextos (estructurados o no).

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

• Data Scientist (generalist denomination that encompasses data management, design and development of artificial intelligence algorithms in any sector).
• Data Engineer (generalist designation that provides hardware and software support to Data Science)
• Software Developer (software engineering in the field of artificial intelligence)
• Web/mobile application developer (data capture, storage, management and visualization)
• Intelligent services designer and developer
• Strategy Engineer (alignment of the organization's strategy with the required technology)
• Data Analytics Manager
• Director of Digital Research and Development
• Digital business leader and strategist
• Digital Business Development Manager
• Director of Digital Innovation, Digital Product
• Digital Marketing Director
• Digital Business Consultant
• Executive Director
• Digital Transformation Director
• Digital Sales Director
• Digital Operations Director