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Facultade de Informática
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Mestrado Universitario en Computación de Altas Prestacións / High Performance Computing (Mod. Presencial)
  Data Analytics with HPC
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Identifying Data 2020/21
Subject (*) Data Analytics with HPC Code 614473108
Study programme
Mestrado Universitario en Computación de Altas Prestacións / High Performance Computing (Mod. Presencial)
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
First Optional 6
Teaching method Hybrid
Department Enxeñaría de Computadores
López Taboada, Guillermo
López Taboada, Guillermo
Rodríguez Álvarez, Gabriel
General description The increasing amount of information available through the Internet calls for the efficient processing of large amounts of data. This has led to the development of new storage and processing techniques to deal with huge amounts of data, namely Big Data techniques, that naturally adapt to distributed systems. The main goal of this subject is to learn suitable processing techniques for large amounts of information in the Big Data world, particularly using the Hadoop ecosystem, and compare these techniques with the traditional ones employed in HPC environments. This will allow the student to select the optimal tools to solve a particular problem.
Contingency plan 1. Modifications to the contents - No changes will be made. 2. Methodologies *Teaching methodologies that are maintained - All. 3. Mechanisms for personalized attention to students - Email: Daily. Of use to make consultations, request virtual meetings to resolve doubts and follow up on supervised work. - CESGA classroom: Daily. According to the needs of the students. They have "thematic forums associated with the modules" of the subject, to formulate the necessary queries. There are also “specific activity forums” to develop the “Directed Discussions”, through which the development of theoretical content of the subject is put into practice. - Teams or the Slack + Jitsi combination: 1 weekly session in a large group for the advancement of the theoretical contents and the tutored works in the time slot assigned to the subject in the faculty class calendar. From 1 to 2 weekly sessions (or more as the students demand) in a small group (up to 6 people), for follow-up and support in carrying out the "supervised work". This dynamic allows for standardized monitoring adjusted to the learning needs of the students to carry out the work of the subject. 4. Modifications in the evaluation - No changes will be made. 5. Modifications to the bibliography or webgraphy - No changes will be made.
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.
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