Master in Computer Engineering

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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

  • Check memory of the Master in engineering Informatcia [PDF]

Educational Guides

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.
  • [English] 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 2017-2018

 

Updated 10 in October of 2017

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 June of 2017