This is the study programme for 2020/2021.

"There are 2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating with the growth of the Internet of Things (IoT). Over the last two years alone 90 percent of the data in the world was generated." (Marr, 2018). In today’s knowledge economy, data is frequently seen as the most crucial resource ("the new oil"). To make sound, justifiable, and informed decisions, managers need to understand how data is generated, collected, processed, and analyzed, as well as quickly access, process, and comprehend up-to-date information on a small- and large-scale basis. This course offers training for such knowledge and skills, through the use of statistical models, real data from around the world, and the R software. Thereby, students will learn how to uncover the hidden patterns and narratives behind data that can inform the decision-makers to make their business sustainable in this data-driven world.

Learning outcome

On completion of the course, students will gain knowledge in:
  • Knowledge of primary data collection strategies and methods
  • Ethical considerations in collecting and working with empirical data
  • Basic programming in R
  • Finding, cleaning & managing varying data sets from different countries
  • Constructing and estimating appropriate statistical models for business issue
  • Making critical business inferences from the data analysis
  • Using R to analyse data and interpret the results to gain insights

Upon completion of this course, students will be able to:
  • Find and extract data
  • Build and manage empirical data sets to support business decision processes
  • Use R to construct a variety of measures, variables, and visualizations
  • Use R to analyze data relevant to business
  • Derive actionable insights and recommendations for business managers


Subject areas that are most likely covered are:
  • Ethics of data collection and analysis
  • Techniques of data gathering and processing
  • Introduction to R
  • Ordinary Least Squares and diagnostics
  • Logistic regression and classification
  • Time series analysis
  • Data visualisations
  • Text analysis
  • Network analysis
  • Data reduction techniques
  • Obtaining data through experiments

Required prerequisite knowledge


Recommended previous knowledge

Appropriate bachelor background in core business fields as well as mathematics for business/economics.


Individual exam and paper from group project
Weight Duration Marks Aid
Individual exam55/100 A - F
Paper from group project45/100 A - F
The course is assessed based on two components. 55% of the final grade is based on an individual in-class digital exam. Students failing the exam will be able to re-take a deferred exam in the same format. The remaining 45% is based on the scientific paper from a group project.

Coursework requirements

Attendance all group consultations with the supervisor, 4 assignments in groups
Students are required to attend all scheduled group consultations with the supervisor.

Course teacher(s)

Course coordinator
Tom Brökel , Yuko Onozaka

Method of work

In this course, you will learn through the mixture of traditional lectures, exercises, seminars, group work and individual study. Lectures provide the basic theoretical knowledge behind the methods. Exercises and seminars are problem-based, and students will learn practical knowledge of (1) basic programming and handling of R; (2) handling of dataset (3) setting up problems and running appropriate statistical models, and (4) gaining insights from the results, through interactions with group members and the instructor(s). Students are required to obtain necessary knowledge by self-studying different materials including videos, textbook chapters, and lecture slides.
Expectations: 560 ECTS work hours divided between lectures, in-class and out-of-class (group) work, seminars, and independent study.

Overlapping courses

Course Reduction (SP)
Research Methods for Business Sciences (MØA103_1) 10
Data Analytics for Business Decisions (MØA104_1) 10

Open to

Master in Accounting and Auditing

Course assessment

Students will have the opportunity to give feedback on the course first in an early dialogue, and in multiple course evaluations. 


Link to reading list

This is the study programme for 2020/2021.

Sist oppdatert: 09.08.2020