R Programming for Data Science

BAN400 R Programming for Data Science

Spring 2021

  • 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 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.

    By mistake, this course description was previously published with a remark that due to overlap, BAN400 could not be combined with BAN401. This was revised on August 13th 2020, and the courses may now be combined without any credit reduction.

  • 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


  • Computer tools

    R, RStudio

  • Literature

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


ECTS Credits
Teaching language

Autumn. Offered Autumn 2020 (first time).

Course responsible

Associate Professor Håkon Otneim, Department of Business and Management Science.

Associate Professor Geir Drage Berentsen, Department of Business and Management Science.

Adjunct Associate Professor Ole-Petter Moe Hansen, Department of Business and Management Science.