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.

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.

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