This is the study programme for 2019/2020. It is subject to change.

MØA103: Research Methods for Business and Social Sciences and MØA104: Data Analytics for Business Decisions is a sequence of methods courses mandatory for first semester master students from all specializations (strategic marketing, innovation, finance, and economics). The former (MØA103) focuses on the scientific process, research design, and introduces the statistical software R and SPSS, whereas the latter (MØA104) focuses on qualitative and quantitative analytical tools relevant for business students.

Learning outcome

On completion of the course, students will have knowledge of:
  • Methods of data preparation
  • Various methods for analyzing and interpreting qualitative business data
  • Various methods for analyzing quantitative business data
  • Pros and cons of methods for preparing, analyzing and presenting qualitative and quantitative data

Upon completion of this course, students will be able to:
  • Assess existing business research
  • Apply data analytics to business problems
  • Derive actionable insights and recommendations for business managers
  • Present research findings to business managers

General competence
This course will contribute to students' general competence in
  • Conducting scientific research
  • Preparing and analyzing business data
  • Using findings to aid business decision-making
  • Presenting and engaging in scientific discussion
  • Group work


Firms have access to various data pertinent to their business. Such data may take a variety of forms, such as in-store and online customer transactions, survey responses, interview and focus group transcripts, published national and industry statistics, stock market transactions, company accounts, newspaper articles, etc. Using real-world applications from various industries, the goal of the course is to familiarize students with different types of data sources and analytical techniques, commonly employed in making effective business decisions. The course involves formulating critical managerial challenges, preparing data for analyses, analyzing data, drawing inferences and telling evidence-based convincing narratives, with a view of yielding actionable results. As such, the course has three key components: (1) Data Preparation, (2) Data Analysis, and (3) Presentation of Results.

Statistical software and programming
Throughout the course, students will continue developing skills on R and SPSS. Although we will use software that are available to students (either the university or the department have licenses or the software is freely available), it is students’ responsibility to install the relevant software to their laptop (if they wish to do so).

Required prerequisite knowledge


Recommended previous knowledge

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


Warning for the Unserious: You are seeking a highly sought-after and competitive MSC degree in business administration from the UiS Business School. Acceptance into the MSC program does not guarantee graduation and diploma. The faculty of the UiS Business School expects that you are highly motivated, possess excellent study habits, behave respectfully of fellow-students and course instructors, and generally show full dedication to your study program.


Group final project and written exam
Weight Duration Marks Aid
Group final project45/1001 A - F1)
Written exam55/1004 hoursA - F1)
The re-examination will be a group project and an individual in-class written exam.
1) One handwritten cheat-sheet (can be written on both sides).

Coursework requirements

Attendance, Assignments

Course teacher(s)

Course coordinator
Yuko Onozaka
Course teacher
Tom Brokel , Espen Olsen , Elisa Thomas

Method of work

Students are expected to attend, be prepared, and actively engage in lectures and learning team work-sessions. The learning teams comprise 4-8 pre-assigned students each. The learning teams will work together on regular assignments and larger projects. A typical week will look as follows:
  1. Monday (lecture): Core materials are presented in a regular lecture style. This is where the methods to analyze the data, both theoretically and practically, are presented. Weekly assignments are also given and discussed here. 3-4 hours.

  1. Wednesday (practical session): The learning teams come together in guided sessions to work on practice problems. 3-4 hours.

  1. Friday (work session): The learning teams come together and work on the assignments. The instructor(s) and/or TAs will be present to aid students. 2 hours.

The total expected workload for each 10 ECTS course is a total of 280 hours distributed approximately as follows: i) lectures (30 hours), ii) student seminars & group work (120 hours), iii) independent study of course material (130 hours).

Open to

Business Administration - Master of Science

Course assessment

Student evaluation will be carried out in accordance with UiS evaluation system.


Saunders, M., Lewis, P and Thornhill, A. 2016. “Research Methods for Business Students (7th Edition)” Pearson Education Limited.

James G., D. Witten, T. Hastie and R. Tibshirani. 2013. “An Introduction to Statistical Learning: with Applications in R” Available at
Additional reading materials

This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 15.12.2019