BAN400 R Programming for Data Science

• Topics

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

  The one-week intensive seminar "BAN420 Introduction to R" constitutes the first part of BAN400 and is an absolute prerequisite for taking BAN400. Please note that BAN420 contains mandatory attendance and activities in the very beginning of the teaching term.

  While BAN420 covers many technical details in R programming, BAN400 goes deeper into the programming structures in R. We will introduce modern software development tools that help solving business and economic problems with R. In addition to the learning outcomes from BAN420, you will in this course carry out complete empirical projects from data collection to end product using tools from the R ecosystem.

  After successfully completing the course, you will be able to use R as your analytical tool to solve a variety of problems in your academic and professional life.

• Learning outcome

  Knowledge: On successful completion, the student can

  • understand the importance and usefulness of R as a tool in data analysis
  • understand the importance of reproducibility in data analysis.
  • understand the importance of documentation when creating scripts.

  Skills: On successful completion, the student can

  • read and understand documentation of packages and functions.
  • use basic data structures (lists, arrays, matrices, vectors and data frames) as appropriate.
  • combine, merge and reshape data sets in R.
  • independently resolve warnings, errors, and other basic programming issues.
  • use functions, loops, assignments, subsetting and conditionals in an R-script.
  • use vectorization, iterations and parallelisation as needed computationally demanding tasks.
  • write documentated and standarised, formatted code as part of code development.
  • use R to program and apply selected prediction and machine learning methods and correctly interpret the output in the relevant context.
  • 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 on campus as well as digitally, and project work in groups.

• Recommended prerequisites

  Basic statistical competence equivalent to MET2 

• Credit reduction due to overlap

  The first part of the course is given as an intensive course, and may be taken separately as BAN420 - Introduction to R.

  Please note that because BAN420 is identical with the first part of BAN400, there is a full credit reduction between the two courses. If you have already passed BAN420 and wish to take BAN400 at a later point, you will be awarded with a total of 7,5 ECTS for the two courses combined.

• Requirements for course approval

  Completed and passed first part of the course (may also be taken separately as BAN420).

  Approved assignments during semester.

• Assessment

  Three day, individual home exam. The three day take-home exam is held between 09:00 at the first day of examination and 14:00 on the third day of examination.

• Grading Scale

  A-F

• Computer tools

  R, RStudio

• Literature

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

  Shorter articles posted on Canvas.