Checking date: 03/05/2019


Course: 2019/2020

Advanced Programming
(17761)
Study: Master in Statistics for Data Science (345)
EPI


Coordinating teacher: ALER MUR, RICARDO

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

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Students are expected to have completed
Programming with R
Competences and skills that will be acquired and learning results.
COMPETENCES THAT THE STUDENT ACQUIRES WITH THIS MATTER CB9 That students 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 That students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous. CG4 Ability to synthesize the conclusions obtained from these analyzes and present them clearly and convincingly in a bilingual environment (Spanish and English) both in writing and orally. CG6 Apply social skills for teamwork and to relate to others autonomously. CE2 Use free software such as Python for the implementation of statistical analysis. CE8 Apply and develop visualization techniques of collected samples with free distribution software such as Python. LEARNING RESULTS THAT THE STUDENT ACQUIRES - Integration of C ++ and R via Rcpp - Python programming language. Machine learning packages. - Brief introduction to the STAN programming language
Description of contents: programme
1) Combination of C ++ with R through Rcpp. 2) Python Language. Graphics in Python (matplotlib and seaborn). Machine learning packages (scikit-learn). 3) Brief introduction to STAN.
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 TEACHING TRAINING METHODOLOGIES OF THE PLAN REFERRED TO MATTERS MD1 Lectures with material and bibliography provided. MD3 Resolution of practical cases, problems, etc. MD5 Preparation reports individually or in groups
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • Dirk Eddelbuettel. Seamless R and C++ Integration with Rcpp (Use R!) . Springer. 2013
  • Julian Avila. scikit-learn Cookbook (2nd edition). Packt. 2017

The course syllabus and the academic weekly planning may change due academic events or other reasons.