Degree | Type | Year |
---|---|---|
2503873 Interactive Communication | OB | 4 |
You can view this information at the end of this document.
It is recommended to have completed the course "Introduction to Big Data" in order to better understand and carry out the course exercises.
Unit 1. Introduction to the scenarios and uses of Big Data.
Unit 2: Study and practice of Big Data architectures (Hadoop/MapReduce-Spark environment).
Unit 3. Cloud solutions and Big Data usage scenarios.
3.1 Geographic intelligence.
3.2 Social analytics
Unit 4. Open data paradigm and public information systems.
The detailed program will be informed on the first day of class through the presentation of the course calendar.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lab practices | 18 | 0.72 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
SEMINARS | 15 | 0.6 | 1, 4, 5, 6, 7, 8, 11, 12, 13 |
THEORETICAL SESSIONS | 15 | 0.6 | 2, 3, 5, 7, 8, 9, 11 |
Type: Supervised | |||
ASSESSMENT | 8.5 | 0.34 | 2, 4, 5, 7, 8, 12, 15, 17, 18, 20, 21, 22 |
TUTORING | 8.5 | 0.34 | 1, 2, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
Type: Autonomous | |||
OTHER ACTIVITIES (study time; practice preparation; seminar preparation, readings, etc.) | 50 | 2 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 22 |
The methodology is based on the following activities:
Theoretical sessions: Introduction theoretical sessions to concepts
Laboratory practices: individual or team works in which practical activities are carried out with one task with time limit. Students must apply knowledge, distribute time and prepare the submission within the classroom and in the hours spent in practice under the professor's guidance.
Seminars: individual or teamwork in which more extensive practical activities are carried out and with tasks open to student creativity. There are no limited time in the classroom, but deadlines for submission. Students must apply knowledge, distribute time and prepare submissions by starting their work within the classroom, but continue it in the form of activities supervised by the professor's team.
Course final work: practical group assessment exercise in which students must solve, during course development, a practical application problem linked to the subject's objectives. Students must raise the problem and perform the four processes to provide a solution based on large amounts of data: search, extraction, analysis and publication of data report including a proposal for a decision based on the information collected and analysed.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
PRACTICAL SESSIONS | 40% | 15 | 0.6 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
STUDENT'S PARTICIPATION | 10% | 8 | 0.32 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
SUBMISSION AND PRESENTATION OF THE COURSE PROJECT | 50% | 12 | 0.48 | 1, 2, 5, 6, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
Activity A. Course project and oral presentation (group) . 50% of the final grade.
Activity B: Laboratory Practice. 40% of the final grade.
Activity C. Student participation. 10% of the final grade.
To approve the subject, it is necessary to get a minimum approval note (5,0) in activities A and B.
RE-EVALUATION:
In the last two weeks of the course, students who have not pass the course can participate in a re-evaluation process consisting of a theoretical test and a practical exercise. Students must have done at least 2/3 of the total course practices (activities gruped on B) and must have obtained an average grade equal to or greater than 3.5 (and less than 5) in all evaluation activities.
PLAGIARISM:
In the event that the student performs any irregularity that may lead to a significant variation of an evaluation act, this evaluation act will be graded with 0, regardless of the disciplinary process that could be instructed. In the event, that several irregularities occur in the evaluation acts of the same subject, the final grade for this subject will be 0.
Basic References
Casas Roma, Jordi (2019) Big data: análisis de datos en entornos masivos. Barcelona: Editorial UOC.
Duran, Xavier (2019). El imperio de los datos: el big data, la privacidad y la sociedad del futuro. PUV Publicacions, Universitat de València: Càtedra de Divulgació de la Ciència, UCC+i, Unitat de Cultura Científica i de la Innovació, Universitat de València.
Dur Lahoz-Beltrá, Rafael (2019). En las entrañas del big data: una aproximación a la estadística. Emse Edapp, S.L.
Fuller A. (2012). The White Book of Big Data. The definitive guide to the revolution in business analytics. Fujitsu. https://www.fujitsu.com/rs/Images/WhiteBookofBigData.pdf
Other references
Tascón, Mario. "Introducción: Big data. Pasado, presente y futuro" Telos: Cuadernos de comunicación e innovación 95 (2013): 47-50. https://telos.fundaciontelefonica.com/archivo/numero095/#contenido
Mohamed, Azlinah, et al. "The state of the art and taxonomy of big data analytics: view from new big data framework" Artificial Intelligence Review 53.2 (2020): 989-1037.
Gandomi, Amir, and Murtaza Haider. "Beyond the hype: Big data concepts, methods, and analytics" International journal of information management 35.2 (2015): 137-144.
Aldana Montes, José Francisco (2018). Introducción al trabajo con datos. Madrid: García-Maroto Editores
Lucivero, Federica. "Big data, big waste? A reflection on the environmental sustainability of big data initiatives." Science and engineering ethics 26.2 (2020): 1009-1030.
This is mostly a practical vourse,thus the required software is the usual one for the performance of capture, processing and analysis tasks in different formats.
Specifically, the following tools are required:
Data analysis software: Excel or similar
Data visualisation software: Infogram - Datawrapper - Flourish
Multimedia editing software: Wordpress - Blogger - Wix
As the subject will carry out practical sessions during all its activities, it is recommended that students (if possible) always bring their laptop to the sessions.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PLAB) Practical laboratories | 61 | Spanish | first semester | afternoon |
(PLAB) Practical laboratories | 62 | Spanish | first semester | afternoon |
(TE) Theory | 6 | Spanish | first semester | afternoon |