Master in Computer Engineering
HomeClick for listings Description of the Master in computer engineering – Big Data Master Professionalizing: official title of the profession Engineer in computer science.
Mandatory external practical work (6 ECTS) Meets the agreement of the Council of Universities, Resolution 8 in June of 2009 the Ministry of Universities (BOE 4 August of 2009, No.. 187) Teaching based on continuous evaluation and projects. Shortcut without additional training for Computer Engineers, Technical Engineering in Computer Systems and Computer Engineering Graduate. In turn, the Master provides direct access to the PhD in Computer Engineering. Verification Memory
- Verification Memory Master in Computer Engineering [PDF]
SubjectsProgram 2019-2020Master in Computer Engineering – Program 2019-2020Link to the page of the grape with the program of the courses The subjects that indicate [Support in English] offer the student materials (Bibliography, transparencies…) in English language, and the possibility of also have tutoring in English, in order to help potential students exchange foreign.
First courseFirst four-month period
- [BigData] Advanced Internet services and applications.
- Quality, Audit and security processes, Services, Resources and Software products.
- Interaction engineering.
- [Support in English] Advanced methods of reasoning and knowledge representation.
- [Support in English] Hardware and Software capture and image display systems.
Second four-month period
- [BigData] Web applications for the search and information management.
- [BigData] [Support in English] Parallel computing and emerging models.
- Strategic technology direction and innovation.
- [Support in English] Financial management of companies and technology-based projects.
- [BigData] [Support in English] Scalable data analysis techniques.
Second courseFirst four-month period
- External practical work.
- Master's Thesis.
Specialty Big Data
- Scalable storage.
- [Support in English] Big Data: business intelligence.
- Technology for Big Data.
Updated 17 July of 2019 Program 2018-2019Master in Computer Engineering – Program 2018-2019Link to the page of the grape with the program of the courses The subjects that indicate [Support in English] offer the student materials (Bibliography, transparencies…) in English language, and the possibility of also have tutoring in English, in order to help potential students exchange foreign.
First courseFirst four-month period
- [BigData] Advanced Internet services and applications.
- Quality, Audit and security processes, Services, Resources and Software products.
- Interaction engineering.
- [Support in English] Advanced methods of reasoning and knowledge representation.
- [Support in English] Hardware and Software capture and image display systems.
Second four-month period
- [BigData] Web applications for the search and information management.
- [BigData] [Support in English] Parallel computing and emerging models.
- Strategic technology direction and innovation.
- [Support in English] Financial management of companies and technology-based projects.
- [BigData] [Support in English] Scalable data analysis techniques.
Second courseFirst four-month period
- External practical work.
- Master's Thesis.
Specialty Big Data
- Scalable storage.
- [Support in English] Big Data: business intelligence.
- Technology for Big Data.
First four-month period – Optional
- Multivariate data analysis.
- Ubiquitous computing.
- Developing practical applications in embedded systems.
- Physical design of large stores of data oriented to the representation of knowledge.
- Fundamentals of Informatics in industry.
- Infrastructure for the development of high-performance computing applications.
- Mathematical methods applied to the development of systems and Internet services.
- Multimodal interaction systems.
- 3D video: Capture, Melting and production of contents 3D using synchronized cameras.
- Web semantics and information extraction.
Updated 20 July of 2018 Program 2017-2018Master in Computer Engineering – Program 2017-2018Link to the page of the grape with the program of the courses The marked subjects such as [English] offer personalized service in English to those students with limited Spanish skills.
First courseFirst four-month period
- [BigData]Advanced Internet services and applications.
- Quality, Audit and security processes, Services, Resources and Software products.
- [English] Interaction engineering.
- Advanced methods of reasoning and knowledge representation.
- [English] Hardware and Software capture and image display systems.
Second four-month period
- Web applications for the search and information management.
- [BigData] [English] Parallel computing and emerging models.
- Strategic technology direction and innovation.
- Financial management of companies and technology-based projects.
- [BigData]Scalable data analysis techniques.
Second courseFirst four-month period
- External practical work.
- Master's Thesis.
Specialty Big Data
- Scalable storage.
- Big Data: business intelligence.
- Technology for Big Data.
First four-month period – Optional
- Multivariate data analysis.
- Ubiquitous computing.
- Developing practical applications in embedded systems.
- Physical design of large stores of data oriented to the representation of knowledge.
- Fundamentals of Informatics in industry.
- Infrastructure for the development of high-performance computing applications.
- Mathematical methods applied to the development of systems and Internet services.
- Multimodal interaction systems.
- 3D video: Capture, Melting and production of contents 3D using synchronized cameras.
- Web semantics and information extraction.
Updated 22 in September of 2017 Educational Guides 2016-2017Master in Computer Engineering – Educational Guides 2016-2017The guides are PROVISIONAL and they can suffer changes in content as well as responsible for the subjects teachers.Show assignments for which guidelines are available only. The marked subjects such as [English] offer personalized service in English to those students with limited Spanish skills.
First courseFirst four-month period Second four-month period Second courseFirst four-month period Specialty Big Data First four-month period – Optional Updated 21 July of 2016 Educational Guides 2015-2016Master in Computer Engineering – Educational Guides 2015-2016The guides are PROVISIONAL and they can suffer changes in content as well as responsible for the subjects teachers
Educational Guides 2014-2015Master in Computer Engineering – Educational Guides 2014-2015The guides are PROVISIONAL and they can suffer changes in content as well as responsible for the subjects teachers
Master's ThesisMaster's ThesisGeneral information From the day 26 may of 2020 the application for the defense of the Master's Degree Work is available at the e-headquarters. A guide to the procedure can be found at: scheme with instructions to follow. The initiation of this procedure is common for all UVa Centres and relates exclusively to the initial application process. Includes three standardized prints: Defence request (stuffed online), The guardian's name and declaration of authorship and originality. After the defense must be sent the memory of the TFM and the request of the student, where the confidential or date of the embargo is listed, libraries at each center. To this end, each Administrative Secretary can agree with the corresponding Library the method that is most of interest to. Procedures for the defense Documents and links Documents for teachers Updated 13 March of 2024 ScheduleCalendar of classes 2020-2021
Degree in computer engineeringHours First Week First course Second course Third course Fourth course
Master in Computer Engineering
Hours First Week First course
INdatHours First Week First course Second course Third course Fourth course Fifth Course Updated 25 March of 2021: added calendar of Grade activities for 2nd grade of the second quarter ExamsCalendar of exams 2020-2021Master in Computer Engineering Degree in computer engineering INdat
Updated 1 in October of 2020: changes to Master's exam schedules
External practical workExternal practical workExternal practical work by the Master Teacher Guide Students of the Master in Computer Engineering have the opportunity to do internships in companies and research groups such as: Business
- aei ciberseguridad
- anfix
- BEONPRICE
- Cognizant
- CORITEL
- CPIICyL
- DATASALT
- EVERIS
- mLEAN
- N
- plasticscm
- roams
- SILVERSTORM
- tecsidel
- Telefónica I d
- voxel 3D
- xeridia
Research groups
- Computer architecture
- Characterization of materials and electronic devices
- Compression, indexing and applications on large collections of data
- Pervasive computing
- Development of ultra-fast functions
- Financial economics and accounting
- Economics and public policy
- Electronics
- Advanced Computing and multimodal interaction systems environments
- Spectroscopy of plasmas and supersonic jets
- GIRO
- Computational Linguistics
- Applied mathematics
- MIOMeT: Inorganic and organometallic molecules with Transition Metals
- Modelling, Biomechanics and advanced visualization of heritage
- Optimization and data enveloping analysis
- Systems thinking and organizational Cybernetics
- Neural networks
- Intelligent systems
- Optimization solutions
- Goblin
- Semantic Web, right of new technologies and computer-related crime
Description of Practices in Company GMVIf you are interested in this offer, send your CV by mail electronic to pe@inf.uva.es, indicating in the mail company/offer in question. CIDAUTCurricular internship related to ICT for research and development projects:
- Industry 4.0. Development of algorithms for analysis of data by applying Machine Learning techniques from the programming language python and Apache Spark platform + MLlib
- Intelligent transport systems. Techniques of artificial vision processing of data/images with application development in C# using Visual Studio Professional and the framework Microsoft .NET with Microsoft SQL Server database.
- Intelligent transport systems. Equipment embedded Linux programming (Raspberry pi/arduino with scripts in Python + OpenCV) development of algorithms for analyzing data on machine vision cameras.
The possibility of a TFM in this context can also be considered. Requirements:
- Basis of programming in C++ and C# (environment of development Visual C# and .NET) and SQL databases: MS SQL Server
- Knowledge of Python and Java
Other knowledge optional:
- OS Linux, shell scripting, BD MySQL
- Version control tools: SVN
- PostGIS and PostgreSQL
- OpenCV
Flexible schedule. It is not necessary to perform any person, You can toggle with tools of video conferencing/email for follow-up through deliverables. Possibility to make practices in summer. If you are interested in this offer, send your CV by mail electronic to pe@inf.uva.es, indicating in the mail company/offer in question. EVERISFunctions:- Participation in the development and implementation of Big Data projects across clients of all sectors( banking, telco,retail,utilities …)
The participation shall include participation in the following activities:
· Data source analysis
· Installing and configuring Big Data platforms
· Implementation of intake mechanisms,
· Industrialization of analytical algorithms
· Developing data processing routines
· Implementing business use cases Technologies Big DataDistribution Cloudera, Horton Works
Tools for Intake of Data: Flume , Sqoop, Kafka
Storage tools: HDFS, Hive, Hbase, Cassandra
Processing tools: Spark, Flink Lines of learning:· What is Big Data and what impact it has on customers?.
· What are the platforms and software for big data processing?
· Analysis and requirements in Big Data projects.
· What is data debugging and identity management?
· What is a Big Data project like?
· How to define a Big Data business case?
· How to analyze information in real time?, How to build predictive models from Big Data?
· How do I build an Integral Dashboard?, What information provides the Big Data to a dashboard?
· What are the most important big data use cases on the market? If you are interested in this offer, send your CV by mail electronic to pe@inf.uva.es, indicating in the mail company/offer in question. BeOnPrice (Data Scientist)Name of the post: Data Scientist BeOnPrice we are a Start-Up technology, which develops solutions for Revenue Management for the tourism sector through technologies of Big Data. In our company we are eager to continue to grow and this is why we have the site for you. Seek optimistic people, you enjoy working in a team, you like challenges and think that everything can be improved. We like the creativity, the commitment, the innovation and the bet by the quality.
If you share these values and your profile fits with the one that we describe below do not doubt that we will have with you. Academic training: Degree in Statistics/Mathematics/Computer science Additional training: Analysis and programming of software. English. Experience: Not necessary but recommended. Roles and responsibilities: Research, modeling, development and implementation of techniques of machine learning Knowledge
– Programming and modeling (Mathematica)
– C++Programming, R, Python
– Distributed processing
– SQL/NO-SQL
– Linux Personal skills
– Initiative and autonomy
– Proactive attitude and dynamic
– Commitment to medium/long-term
– Work in a team
– Communication skills
– Strategic mindset
– Capacity of leadership
– Pragmatism
– Creativity
– Tolerance to ambiguities Contact
BEONPRICE
info@beonprice.com
923 100 220 If you are interested in this offer, send your CV by mail electronic to pe@inf.uva.es, indicating in the mail company/offer in question. VOXEL 3DIntroduction to the problem VOXEL 3D we are dedicated to creating services of spatial information on the Internet. We are content providers of georeferenced image to a third party, companies or individuals. Our services are continuously monitored anonymously through logs of access in standard formats. These logs are recurrent, and presented interesting information about the use that they give the customers to their data, from the point of view geospatial. All the information which VOXEL 3D serves its customers is georeferenced (typically, each data has its correspondence geographic, latitude – longitude), and it is feasible to study to know the behaviour of our customers. In addition, VOXEL3D, has developed its own tool for collecting geospatial analytical applications of web mapping (typically Google Maps, or the prolific libraries current Leaflet and Openlayers 3), to learn how to use a common user anonymously or not, the existing content in a web map, or a geographical application online based on these systems. This tool allows you to collect any type of interaction of users with information related to maps on the web, generating log files of the information of interest. The information generated is valuable to know the interests of the clients of these applications. Objectives of the work The general objective is to develop a platform for the collection of KPI’s (key perfomance indicator) and key indicators of online maps that allow the automatic collection of the information of online maps, storage, indexing the information from the logs and creating a dashboard to display the information to facilitate decision making. This work is part of the tools of web map analytics which is developing VOXEL 3D today. Work Plan PHASE: Immersion in the problem [60h] A. 1 – Reading basic bibliography, knowledge of the problem. A. 2 – Knowledge of the state of the art advanced tools of web mapping and web analytics. A. 3 – Detailed study of the KPI’s interesting for a web application mapping. PHASE B: Development [210h] B. 1 the Process of analysis and design of the modules of the system B. 2 Processing, indexing and storage of information on the basis of logs of use. B. 3 Development of the dashboard control. PHASE C: Conclusions [30h] Technology Stack It is anticipated that work with the following tools/technologies: * Python/R for processing and statistical analysis. * Elasticsearch for search and indexing of information. * Python/Logstash for processing logs. * Introduction to monitoring tools like Kibana vs GreyLog. * ToroDB/PostreSQL/MongoDB as storage systems. If you are interested in this offer, send your CV by mail electronic to pe@inf.uva.es, indicating in the mail company/offer in question. Related CompaniesVisiting ProfessorsNombre | Empresa/
Universidad | Asignatura | Profesor
Responsable | Curso/s | Alfageme Sainz, Samuel
| Telefónica I+D | Calidad, Auditoría y Seguridad de procesos, servicios, recursos y productos software | Yania Crespo González-Carvajal | 2018/19 | Alonso Moreno, Juan | Tecnilógica | Ingeniería de la Interacción | Alejandra Martínez Monés | 2015/16 2016/17 2017/18 | Aparicio Lázaro, Álvaro
| Dinamiza Consulting | Big Data: Inteligencia de Negocios | Quiliano I. Moro Sancho y Carlos Vivaracho Pascual (Carlos 2017/2018) | 2016/17 2017/18 | Álvarez Hernando, Francisco Javier | AC Abogados | Big Data: Inteligencia de Negocios | Quiliano I. Moro Sancho y Carlos Vivaracho Pascual (Carlos 2017/2018) | 2016/17 2017/18 2018/19 | Arroyo Álvarez, Julián | Ayuntamiento
de Valladolid | Desarrollo
de aplicaciones web | César
González Ferreras | 2013/14 2014/15 | Bayón Fernández, Andrés
| Anfix | Aplicaciones web para la búsqueda y gestión de información | César González Ferreras | 2018/19 | Boydens, Joroen | Universidad KU Leuven (Bélgica) | Computación Paralela y Modelos Emergentes | Benjamín Sahelices Fernández | 2015/16 | Burget, Radek | Brno University of Technology
Brno (Rep. Checa) | Desarrollo
de aplicaciones web | César González Ferreras | 2014/15 | Caballero Muñoz-Reja, Ismael | Universidad Castilla La Mancha | Calidad, Auditoría y Seguridad de procesos, servicios, recursos y productos software | Esperanza Manso (2016/17 y 2017/18) Yania Crespo González-Carvajal (2018/19) | 2016/17 2017/18 2018/19 | Carretero Guarde, José Ignacio | Telefónica I+D (Fiware) | Computación Paralela y Modelos Emergentes | Benjamín
Sahelices
Fernández | 2015/16 2016/17 2017/18 2018/19 | De Francisco Marcos, David | Indra | Gestión
económico financiera de empresas y proyectos de base
tecnológica | Alfredo Martínez Bobillo (de 2013 a 2017) y Fernando Tejerina Gaite (desde 2017/18) | 2013/14 2014/15 2015/16 2016/17 2017/18 2018/19 | Domínguez Collar,
Marta | Everis | Almacenamiento escalable | Miguel Ángel Martínez
Prieto, Aníbal Bregón
Bregón y Fernando Díaz Gómez (Fernando 2017/2018) | 2016/17 2017/18 | Erro Eslava,
Daniel | Universidad
del País Vasco | Interacción
multimodal | David
Escudero Mancebo | 2014/15 2015/16 | Gañán Alonso, Patricia
| Anfix | Desarrollo
Aplicaciones
Web | César
González
Ferreras | 2015/16 | García Morán, Daniel | Devo | Computación Paralela y Modelos Emergentes | Benjamín
Sahelices
Fernández | 2018/19 | Gómez Cuadrado, Julián
| Analyticae | Big Data: Inteligencia de Negocios | Quiliano I. Moro Sancho | 2018/19 | Khadmaoui,
Amine
| Anfix | Desarrollo
Aplicaciones
Web | César
González
Ferreras | 2015/16 | Ibáñez Pascual, Antonio | Junta de Castilla y León | Web Semántica y Extracción de Información | Mercedes Martínez González | 2015/16 | Izquierdo Martín, Isabel María | Telefónica | Computación Paralela y Modelos Emergentes | Benjamín
Sahelices
Fernández | 2018/19 | Lerma Brines, María del Sol
| Beonprice | Tecnología para el Big Data (octubre) | César González Ferreras e Iván Santos Tejido (Iván 2017/2018) | 2016/17 2017/18 2018/19 | Marín de la Iglesia, José Luis | Gateway S.C.S. | Web Semántica y Extracción de Información | Mercedes Martínez González | 2015/16 | Muñoz García, Alberto
| Xeridia | Tecnología para el Big Data (octubre) | César González Ferreras e Iván Santos Tejido (Iván 2017/2018) | 2016/17 2017/18 2018/19 | Nikitas
Assimakopoulos | University
Piraeus (Grecia) | Dirección estratégica de la tecnología y la investigación | José Manuel Pérez Ríos | 2013/14 | Pascual Sancho, Jorge
| Anfix | Aplicaciones web para la búsqueda y gestión de información | César González Ferreras | 2016/17 2017/18 | Puga
Coelho, Joel J. | University
Beira Interior (Portugal) | Computación paralela y modelos emergentes | Agustín de Dios Hernández | 2013/14 | Puga
Coelho, Joel J. | University
Beira Interior (Portugal) | Sistemas Hardware y Software de Captura y Visualización de Imagen | Helena Castán Lanaspa | 2014/15 | Requejo Tovar, Jaime | IBM | Big Data: Inteligencia de Negocios | Quiliano I. Moro Sancho y Carlos Vivaracho Pascual (Carlos 2017/2018) | 2016/17 2017/18 | Rodríguez Martín, Javier | Telefónica I+D | Tecnología para el Big Data (noviembre) | César González Ferreras e Iván Santos Tejido (Iván 2017/2018) | 2016/17 2017/18 | Rodríguez Monje, Moisés | AQC Lab | Calidad, Auditoría y Seguridad de procesos, servicios, recursos y productos software | Esperanza Manso (de 2015 a 2017) Yania Crespo González-Carvajal (desde 2017/18) | 2015/16 2016/17 2017/18 2018/19 | Sánchez Castelló, Daniel | Everis | Almacenamiento escalable | Aníbal Bregón Bregón | 2018/19 | Serrano Taylor, Yanelis
| Universidad de Salamanca | Big Data: Inteligencia de Negocios | Quiliano I. Moro Sancho | 2018/19 |
Specialty Big DataThe course 16/17 It begins the BigData specialty in the Master in computer engineeringWith the entry of the new specialty, the division into ECTS credits is as follows: The duration is of 90 ECTS (a course and a quarter)
- First course: 60 ECTS in subjects required
- 12 ECTS in management
- 48 ECTS of computer technology with training in Big Data
- Second course:
- General route: 15 Optional ECTS
- Big Data: 15 Specialty elective ECTS BigData
- 6 ECTS credits in internship and
- 9 ECTS at the end of Master work
It encourages participation in company with the dual education company + University: mornings will work in a company-paid form and in the evenings there will be teaching from the Master. Intends that you a time completed the Master, continue working in the company. Overview of the contents of the subjects: Subject | Content | Observations | Technology for Big Data | Languages for Big Data | R, Python | Distributed analysis tools | Introduction to MapReduce Apache Hadoop Apache Spark Hadoop ecosystem: Pig, Sqoop, Storm Apache Mahout | Infrastructure for Big Data | Cluster Linux for Big Data Monitoring | Cloud Platforms | Amazon's Elastic Compute Cloud (Amazon EC2) Google App Engine Microsoft Azure OpenStack | Scalable storage | Scalability | Sharding and database clusters | HDFS storage | Hive | Storage in database NoSQL | Key/value storage Document storage Storage graph Big-table | Technologies | Cassandra, MongoDB, CouchDB, Neo4J, GraphDB, HBase, Elasticsearch | Big Data: business intelligence | Big data in decision support systems | | Creation of reports and scorecards | | Cases of integration of information sources | Preprocesado Web Data Big data in multimedia systems | Legal aspects and ethical regulators | |
Sign upMaster in Computer Engineering: SignupCourse 2019-2020
(pre-registration is not required to register) Compatibility with work:
– Afternoon
– Possibility of being “part-time student” Updated 11 July of 2019 | |