Curso big data uned
uned spain
Hold a degree, bachelor’s degree, diploma, technical engineer or technical architect. The course director may propose the establishment of additional requirements of specific previous training in some disciplines.
The degree has a clear professional projection, oriented to the company, but it also includes an academic aspect, establishing the theoretical bases and opening research frameworks in highly topical subjects.
The master’s degree includes the occasional collaboration of different professionals from the sector throughout the program, such as: Other InformationThe information provided in the following list of hyperlinks is the sole responsibility of the Teaching Team. In case of any contradiction, the training offer approved by the Governing Council for each call will prevail, as well as the Regulation of Lifelong Learning and the rest of the University legislation in force.Web page 2. Fundamentals of Big Data
ie global master in business analytics and big data
MODULAR COURSEUniversity Expert Diploma, Specialist Diploma and Master’s Degree The courseInformation is the basis of today’s society. Data and data processing have become a critical aspect in all areas of knowledge.Read more ModularThe course is structured in a modular way: depending on the credits taken, you will obtain the Expert Diploma, the Specialization Diploma and finally the Master’s Degree. CalendarEnrollment periods: from September 7 to November 15, 2021. Enrollment extension from March 1 to March 25, 2022. The Program begins on January 1, 2022 and ends on December 31, 2022.
ExpertDiploma de Experto Universitario: 15 ECTS credits Duration: from January 1 to March 30, 2022Cost (including materials): 975 € SpecialistDiploma de Especialización: 30 ECTS creditsDuration: from January 1 to June 31, 2022 (at least, depending on the Module in which the student enrolls)Cost (including materials): 1,950 €.
join big data
Only for programs offering Master’s, Specialist or Expert degrees or diplomas, the student must be in possession of a degree, bachelor’s degree, diploma, technical engineer or technical architect. The course director may propose the establishment of additional requirements of specific prior training in some disciplines.
– In the Specialization Diploma it is compulsory to enroll in modules 0001, 0002, 0003, 0004 and 0005. To obtain the 30 credits the student must choose and enroll in one more module among the remaining ones.
– In the Master’s program it is mandatory to enroll in modules 0001, 0002, 0003, 0004, 0005 and 0015 (TFG). To obtain the 60 credits the student must choose and register for 5 more modules among the remaining ones3. Methodology and activitiesThe methodology will be distance learning, so it will not be mandatory to attend classes or lectures, allowing students to combine the course with other activities and in any geographical location.
máster en ciencia de datos españa
Los datos de los alumnos y las prácticas de aprendizaje son esenciales para alimentar los sistemas de inteligencia artificial utilizados en la educación. Los datos recurrentes entrenan a los algoritmos para que puedan adaptarse a nuevas situaciones, ya sea para optimizar el trabajo del curso o para gestionar tareas repetitivas. A medida que los algoritmos se extienden en diferentes contextos de aprendizaje y se amplían las acciones que realizan, se requieren marcos interpretativos pedagógicos para utilizarlos adecuadamente. A partir del análisis de casos y de una revisión bibliográfica, el artículo analiza los límites de las prácticas de aprendizaje basadas en el uso masivo de datos desde un enfoque pedagógico. Se centra en la captura de datos, los sesgos asociados a los conjuntos de datos y la intervención humana tanto en el entrenamiento de los algoritmos de inteligencia artificial como en el diseño de los pipelines de aprendizaje automático. Para facilitar el uso adecuado de las prácticas de aprendizaje basadas en datos, se propone enmarcar una heurística apropiada para determinar la idoneidad pedagógica de los sistemas de inteligencia artificial y también su evaluación tanto en términos de responsabilidad como de calidad del proceso de enseñanza-aprendizaje. Así, finalmente, se discute un conjunto de reglas propuestas de forma descendente que pueden contribuir a llenar las lagunas identificadas para mejorar el uso pedagógico de los algoritmos educativos basados en datos.
