Master in Computer Engineering

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    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]

    Educational Guides

    Program 2018-2019

    Master in Computer Engineering – Program 2018-2019

    Link 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 course

    First 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 2017-2018

    Master in Computer Engineering – Program 2017-2018

    Link 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 course

    First 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 course

    First 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 2016-2017

    Master in Computer Engineering – Educational Guides 2016-2017

    The 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.

    Updated 21 July of 2016

    Master's Thesis

    Schedule

    Calendar of classes 2018-2019

    Master in Computer Engineering

    • Home first semester: 17 September
    • End of the first semester: 11 January
    • Home second semester: 4 February
    • End second semester: 24 may

     

    Updated 11 in September of 2018

    Exams

    External practical work

    External practical work

    External 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

    GMV

    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.

    CIDAUT

    Curricular 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.

    EVERIS

    Functions:- 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:
    · Analysis of sources of data
    · Installation and configuration of platforms in Big data
    · Implementation of mechanisms of intake,
    · Industrialization of analytics algorithms
    · Development of routines for data processing
    · Implementation of business use casesTechnologies Big DataDistribution Cloudera, Horton Works
    Tools for Intake of Data: Flume , Sqoop, Kafka
    Storage tools: HDFS, Hive, Hbase, Cassandra
    Processing tools: Spark, FlinkLines of learning:· What is Big Data and what impact it produces in the customers?.
    · What are the platforms and the software for processing Big Data?
    · Analysis and decision requirements in Big Data projects.
    · What is the debugging of data and identity management?
    · How is a project of Big Data?
    · How to define a business case for Big Data?
    · How to analyze information in real-time?, How to build predictive models from Big Data?
    · How do you build a balanced scorecard?, What information provides the Big Data to a dashboard?
    · What are the use cases of Big Data's most important 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 3D

    Introduction 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 Companies

    master_empresas_aeiseguridadmaster_empresas_anfixmaster_empresas_beonpricemaster_empresas_cognizantcoritelmaster_empresas_cpiicylDatasaltEVERISmleannmaster_empresas_plasticscmRoamsSILVERSTORMmaster_empresas_tecsidelmaster_empresas_telefonica_i + dvoxel3dmaster_empresas_xeridia

    Visiting Professors

    Nombre

    Empresa/
    Universidad

    Asignatura

    Profesor
    Responsable

    Curso/s

    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

    Arroyo Álvarez, Julián

    Ayuntamiento
    de Valladolid

    Desarrollo
    de aplicaciones web

    César
    González Ferreras

    2013/14

    2014/15

    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

    2017/18

    Carretero Guarde, José Ignacio

    Telefónica I+D

    Computación Paralela y Modelos Emergentes

    Benjamín
    Sahelices
    Fernández

    2015/16

    2016/17

    2017/18

    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 (2017/2018)

    2013/14

    2014/15

    2015/16

    2016/17

    2017/18

    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

    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

    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

    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

    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

    2015/16

    2016/17

    2017/18

    Specialty Big Data

    The course 16/17 It begins the BigData specialty in the Master in computer engineering

    With 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:

    SubjectContentObservations
    Technology for Big Data Languages for Big DataR, Python
    Distributed analysis toolsIntroduction to MapReduce

    Apache Hadoop

    Apache Spark

    Hadoop ecosystem: Pig, Sqoop, Storm

    Apache Mahout

    Infrastructure for Big DataCluster Linux for Big Data

    Monitoring

    Cloud PlatformsAmazon's Elastic Compute Cloud (Amazon EC2)

    Google App Engine

    Microsoft Azure

    OpenStack

    Scalable storage ScalabilitySharding and database clusters
    HDFS storage Hive
    Storage in database NoSQLKey/value storage

    Document storage

    Storage graph

    Big-table

    TechnologiesCassandra, 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 up

    Master in Computer Engineering: Signup

    Compatibility with work:
    – Afternoon
    – Possibility of being “part-time student”

    Updated 2 in May of 2018