Checking date: 13/05/2021

Course: 2021/2022

Scalable and Distributed Computing
Study: Master in Statistics for Data Science (345)

Coordinating teacher: CARRETERO PEREZ, JESUS

Department assigned to the subject: Department of Computer Science and Engineering

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Programming in R Advanced Programming
Knowledge acquisition of: 10) Parallel computing; 11) parallel compiling yn Python and R; 3) Cloud computing platforms; 4) Big Data distributed architectures.
Skills and learning outcomes
Description of contents: programme
1) Parallel computing. Fundamentals and paradigms. 2) Parallelising Big Data Applications. 3). Cloud computing. 4). Big Data platforms. 5). HADOOP y SPARK. 6) MapReduce paradigms. 7) NoSQL storage systems 8) CUDA and OpenCL for Google TensorFlow. 9) Applications.
Learning activities and methodology
TRAINING ACTIVITIES OF THE STUDY PLAN REFERRED TO MATTERS AF1 Theoretical class AF2 Practical classes AF4 Laboratory practices AF5 Tutorials AF6 Group work AF7 Individual student work AF8 Face-to-face evaluation tests Code Activity No. Total hours No. Presential hours% Presence Student AF1 55 55 100 AF2 25 25 100 AF4 25 25 100 AF5 20 20 100 AF6 50 0 0 AF7 192.5 0 0 AF8 7.5 7.5 100 TOTAL MATTER 375 125 33 TEACHING TRAINING METHODOLOGIES OF THE PLAN REFERRED TO MATTERS MD1 Presentations in the teacher's class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD3 Resolution of practical cases, problems, etc. ¿posed by the teacher individually or in groups MD5 Preparation of papers and reports individually or in groups
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • Jeffrey Aven . Data Analytics with Spark Using Python . Addison-Wesley Data & Analytics Series. 2018

The course syllabus may change due academic events or other reasons.

More information: