BAN400 R Programming for Data Science

• Topics

  R is among the most powerful and widely used programming languages for data analysis in science and business. R is a free open-source tool, and new packages and functionalities are continuously added.

  The course is intended for students without prior experience with R or other programming languages. The course is split into two main parts. In the first part, you will learn the basics of R programming by

  • setting up your own R programming environment on your personal computer using Rstudio,
  • learning how to write, execute, and modify R code and R scripts,
  • loading data sets into R, creating compelling numerical and graphical summary statistics and seeing how to use R to perform some standard statistical analyses, and
  • using programming techniques such as loops, conditionals, and functions to effectively solve practical and analytical issues we encounter when working with data.

  In the second part, you will dive deeper into selected topics in applying R for solving common data science problems. You will learn important and general programming techniques such as debugging, version control, and parallel computing, and you will be using R to perform advanced data analysis and visualizations. After successfully completing the course, you will be able to use R as your analytical tool to solve various problems in your academic and professional life.

• Learning outcome

  Knowledge

  On successful completion, the student

  • Understands the importance and usefulness of R as a tool in data analysis.
  • Understands the importance of reproducibility and version control in data analysis.

  Skills

  On successful completion, the student can

  • Read and understand documentation of packages and functions.
  • Use basic data structures (lists, vectors, and data frames) as appropriate.
  • Combine, merge, and reshape data sets in R.
  • Independently resolve warnings, errors, and other fundamental programming issues.
  • Use functions, loops, assignments, subsetting, and conditionals in an R-script.
  • Use vectorization, iterations, and parallelization as needed for computationally demanding tasks.
  • Write documented and standardized, formatted code as part of code development.
  • Create and export convincing tables and figures for use in reports and presentations.
  • Apply R to empirical business and economics problems.

  General competence

  On successful completion, the student can

  • Work efficiently in R and RStudio.
  • Conduct reproducible data analysis with R.

• Teaching

  Plenary tutorials and project work.

• Recommended prerequisites

  Basic statistical competence equivalent to MET2 

• Credit reduction due to overlap

  There is a full credit reduction between BAN400 and the 2,5 ECTS seminar BAN420, which is no longer offered. This means that if you have already passed BAN420 and complete BAN400 at a later point, you will be awarded a total of 7,5 ECTS for the two courses combined.

• Compulsory Activity

  One assignment must be completed and approved for course approval.

• Assessment

  6 hour digital school exam with access to R and RStudio

• Grading Scale

  A-F

• Computer tools

  R, RStudio

• Literature

  R for Data Science by Hadley Wickham, available at https://r4ds.had.co.nz/

  Links to special topics will be posted on Canvas

• Permitted Support Material

  One bilingual dictionary (Category I)

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support material https://www.nhh.no/en/for-students/regulations/

  and https://www.nhh.no/en/for-students/examinations/examination-support-materials