Checking date: 07/06/2019


Course: 2019/2020

Big Data for Space Missions
(18103)
Study: Master in Space Engineering (360)
EPI


Coordinating teacher: MOLINA BULLA, HAROLD YESID

Department assigned to the subject: Department of Signal and Communications Theory

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Competences and skills that will be acquired and learning results.
Basic competences CB6 To 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 Students must know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study CB8 Students must be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments CB9 Students must know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialized and non-specialized audiences in a clear and unambiguous way CB10 Students must have the learning skills allowing them to continue studying in a way that will be largely self-directed or autonomous. General competences CG1 Capacity for the formulation, critical verification and defense of hypotheses, as well as the design of experimental tests for verification. CG5 Ability to handle the English, technical and colloquial language. Specific competences CE15 Ability to develop a professional activity in an organization, being aware of the business and enterprise context.
Description of contents: programme
Common topics: It is considered relevant for the present program that students can shape part of their space engineering curriculum according to their interests and motivations, in a personalized way. To this end, this subject includes mainly a set of optional subjects. The optionality also has a double benefit: it allows first to monitor the topics of greater demand and interest on the part of the students and secondly to adapt every few years the offer of courses to the new trends in space engineering. Given that the number of elective courses is equivalent to 5 of 3 ECTS each, the offer of the master will be equivalent to 10 courses of 3 ECTS. A minimum number of students enrolled is required for the courses to take place. This number cannot be, in any case, higher than 50% of students enrolled in the master. In-company internships are offered within this subject, optionally. In the same way, students will be able to participate in supervised development projects, in which they would work in a practical and specialized way some of the aspects dealt with in the previous subjects (1-4). In the same way, those subjects of other masters that cover topics of interest for space engineering will also be considered within this matter. Finally, this matter will include, within the optional offer, regulated mentoring of students by professionals in the space sector. Specific topics to each subject: Big Data for Space Missions. The program of this subject includes: statistics for data analysis; technological fundamentals in the Big Data world; optimization for large-scale data; machine learning; data analytics. 3. Big Data Processing a. Supervised Machine Learning for Data Transmission b. Unsupervised Machine Learning for Data Transmission c. Data Batch Processing (Hadoop / Spark) d. Data Processing in Streaming (Spark/Storm/Flink) e. Big Data Storage and Management f. Balancing Processes Architectures in backends varnish, kafka g. Scalable Big Data Stora in NoSQL Databases cassandra / Hbase
Learning activities and methodology
AF1 Theoretical class AF2 Practical classes AF3 Practices in computer classroom AF4 Laboratory practices AF6 Group work AF7 Individual student work AF8 Evaluation activities Code activity Nº Total hours Nº HoursPresencial % Student's presence AF1 120 120 100 AF2 60 60 100 AF3 15 15 100 AF4 15 15 100 AF6 100 0 0 AF7 430 0 0 AF8 20 20 100 TOTAL MATERIA 760 230 30
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Basic Bibliography
  • George, Lars. HBase: The Definitive Guide. O'Reilly.
  • H. Karau, A. Konwinski, P. Wendell, and M. Zaharia,. Learning Spark¿: Lightning-Fast Big Data Analysis. O'Reilly. 2015
  • Sandy Ryza . Advanced analytics with spark¿: patterns for learning from data at scale. O'Reilly. 2015
Additional Bibliography
  • PENTREATH, N. y PAUNIKAR, A. Machine learning with Spark : create scalable machine learning applications to power a modern data-driven business using Spark. Packt Publishing. 2015
Recursos electrónicosElectronic Resources *
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The course syllabus and the academic weekly planning may change due academic events or other reasons.