This course is designed to provide students with hands-on experience and proficiency in utilizing the R programming language for statistical analysis, data visualization, and data manipulation. Participants will engage in practical exercises and real-world applications to develop a comprehensive understanding of R's capabilities.

Temps présentiel : 25 heures

Charge de travail étudiant : 100 heures

Méthode(s) d'évaluation : Evaluation - Examen final, Evaluation - Examen partiel

Référence :
Hadley Wickham and Garrett Grolemund. (2017). "R for Data Science." O'Reilly Media. Bradley Boehmke. (2016). "Data Wrangling with R." O'Reilly Media. Andy Field, Jeremy Miles, and Zoë Field. (2012). "Discovering Statistics Using R." SAGE Publications. Hadley Wickham. (2016). "ggplot2: Elegant Graphics for Data Analysis." Springer. Hadley Wickham. (2015). "R Packages." O'Reilly Media. Yihui Xie, J.J. Allaire, and Garrett Grolemund. (2018). "R Markdown: The Definitive Guide." Chapman and Hall/CRC. Hadley Wickham. (2020). "Mastering Shiny." O'Reilly Media. Foster Provost and Tom Fawcett. (2013). "Data Science for Business." O'Reilly Media.