Competencies and objectives
Course context for academic year 2020-21
The course Infrastructures and Technologies of Big Data is proposed as one of the 4 optional of the first semester of the Master. It is a course with objectives and contents related to computing, specifically computer architectures. A recommended profile for access to the Master in Data Science is that of graduates with skills in Computer Engineering. The subject Big Data Infrastructures and Technologies is highly recommended for students of the Master's degree in computer science profile who need to complete their training in computer architecture and technologies especially oriented to the processing of large amounts of data and high performance. The course also provides the knowledge and skills related to infrastructures and technologies needed to realistically address the designs and solutions proposed in the rest of the curriculum.
Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2020-21
Transversal Competences
- CT1 : Ser capaz de liderar proyectos relacionados con la Ciencia de Datos, así como dirigir equipos de trabajo
- CT2 : Mostrar competencias informáticas e informacionales en el ámbito de la ciencia de datos.
- CT3 : Reunir competencias en comunicación oral y escrita.
General Competences
- CG1 : Aplicar los conocimientos adquiridos a problemas reales relacionados con la ciencia de datos.
- CG12 : Ser capaz de desarrollar experimentos, procesos, instrumentos, sistemas, infraestructuras durante todo el ciclo de vida de los datos.
- CG2 : Ser capaz de desarrollar y aprender de forma auto-dirigida o autónoma temas relacionados con la ciencia de datos.
- CG3 : Saber desenvolverse en contextos multidisciplinares y/o internacionales aportando soluciones desde el punto de vista de la ciencia de datos.
- CG6 : Ser capaz de adaptarse a entornos relacionados con la ciencia de datos, fomentando el trabajo en equipo, la creatividad, la capacidad crítica y el espíritu emprendedor.
- CG7 : Ser capaz de adaptarse al ambiente cambiante propio de la disciplina y de comprender y aplicar los nuevos avances técnicocientíficos relacionados con la ciencia de datos.
- CG8 : Saber proyectar, diseñar, desarrollar, implantar y mantener productos, aplicaciones y servicios relacionados con la ciencia de datos, teniendo en cuenta aspectos técnicos, económicos y de eficiencia.
- CG9 : Saber dirigir proyectos relacionados con la ciencia de datos, cumpliendo la normativa vigente y asegurando la calidad del servicio.
Specific Competences
- CE9 : Utilizar y manejar eficazmente las infraestructuras y servicios big data. Utilizar y aplicar estos servicios para soportar y realizar toma de decisiones basadas en datos.
Basic Competences
- CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
- CB6 : Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
- CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
- CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
- CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.
Learning outcomes (Training objectives)
No data
Specific objectives stated by the academic staff for academic year 2020-21
• Know the technological alternatives for processing, managing and storing BigData applications.
• Define the technology requirements for running distributed and cloud-based applications from a given design.
• Evaluate the performance difference between different distributed and cloud-based platforms to be able to recommend a solution.
• Model solutions to be able to use distributed and cloud-based platforms.
General
Code:
43451
Lecturer responsible:
FUSTER GUILLO, ANDRES
Credits ECTS:
6,00
Theoretical credits:
1,20
Practical credits:
1,20
Distance-base hours:
3,60
Departments involved
-
Dept:
INFORMATION TECHNOLOGY AND COMPUTING
Area: COMPUTER ARCHITECTURE
Theoretical credits: 1,2
Practical credits: 1,2
This Dept. is responsible for the course.
This Dept. is responsible for the final mark record.
Study programmes where this course is taught
-
UNIVERSITY MASTER'S DEGREE IN DATA SCIENCE
Course type: OPTIONAL (Year: 1)