Checking date: 15/05/2021

Course: 2021/2022

Data Tidying and Reporting
Study: Master in Statistics for Data Science (345)


Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Programming in R Programation advance
** COMPETENCES THAT THE STUDENT ACQUIRES WITH THIS MATTER CB06 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 CB09 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. CG04 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. CG06 Apply social skills for teamwork and to relate to others autonomously. CG07 Apply the advanced techniques of analysis and representation of information, in order to adapt it to real problems. CE02 Use free software such as R and Python for the implementation of statistical analysis. CE08 Apply and develop visualization techniques of samples collected with freely distributed software such as R and Python. CE14 Apply knowledge and advanced statistical consulting skills. ** LEARNING RESULTS THAT THE STUDENT ACQUIRES Acquisition of knowledge on: 1) perspective of the applications and cases of use of statistics nowadays in the business environment; 2) analytical skills needed in a statistical consulting service; 3) the tidyverse environment for the management of databases and creation of graphics; 4) techniques of automatic presentation of statistical results in reports; 5) development of remote data processing servers such as Shiny.
Skills and learning outcomes
Description of contents: programme
Versioning - Introduction to git - Working with git and GitHub Introduction to HTML - HTML - CSS style - Javascript Data plotting in Javascript - d3.js - plotly.js Dynamic presentations - R Presentations - reveal.js Introduction to R Shiny Introduction to Python Dash
Learning activities and methodology
** TRAINING ACTIVITIES AF1 Theoretical class AF2 Practical classes AF4 Laboratory AF5 Tutorials AF6 Group work AF7 Individual work ** TRAINING TEACHING METHODOLOGIES MD1 Frontal lessons with support of computer and audiovisual media, in which the main concepts of the subject are developed. IN addition the bibliography is provided to complement the students' learning. MD3 Resolution, individually or in group, of practical cases, problems, etc., raised by the teacher MD5 Individual or in group preparation of papers and reports
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Hadley Wickham, Garrett Grolemund. R for Data Science. O'Reilly Media. December 2016
  • Keon-Woong Moon. Learn ggplot2 Using Shiny App (Use R!). Springer. 2016
Additional Bibliography
  • Chris Beeley. Web Application Development with R Using Shiny. Packt Publishing. 2013
  • Winston Chang. R Graphics Cookbook: Practical Recipes for Visualizing Data. O'Reilly Media. 2013

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