R Programming for Data Science

BAN400 R Programming for Data Science

Spring 2020

  • 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 analysis techniques in data science and machine learning that will be programmed in R and applied to business and economic problems. 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

    To be announced.

  • Credit reduction due to overlap

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

  • Assessment

    To be announced.

  • Grading Scale

    To be announced.

  • Computer tools

    R, RStudio

  • Literature

    To be announced.


ECTS Credits
Teaching language

Fall. First time autumn 2020.

Course responsible

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

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

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