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

  • use functions, loops, assignments, subsetting and conditionals in an R-script.
  • 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.
  • create and export convincing tables and figures for use in reports and presentations.
  • independently resolve warnings, errors, and other basic programming issues
  • apply R to empirical business and economics problems.
  • use R to program and apply selected prediction and machine learning methods and correctly interpret the output in the relevant context.

  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 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.

  Due to overlap, this course can not be combined with BAN401 - Applied programming and data analysis for business.

• Requirements for course approval

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

• Assessment

  Term paper (written in groups of 2-4 students, during the semester). The assignment is handed out at the start of the semester and handed in at the end.

• Grading Scale

  Pass/fail

• Computer tools

  R, RStudio

• Literature

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