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Marketing Analytics

This is the study programme for 2020/2021.

How can businesses use data to make informed decisions? How can organizations extract useful information out of the vast amount of data? This course teaches students how to apply various analytical tools to make sense out of data while exploring real world business challenges. Students will learn basic programming in software R, and run various models, from traditional regression to simple machine learning algorithms.

Learning outcome

Upon completion of this course, students should gain the knowledge of:
  • the fundamental data analytic techniques, such as various regression models, classification and segmentation tools
  • how to obtain business insights from data analytics

Upon completion of this course, students will be able to:
  • identify and carry out the appropriate analytical method for a given problem
  • draw scientifically sound conclusions from analytical results
  • write and execute simple R programs for fundamental statistical analyses
  • independently develop and carry out their own research projects and communicate results from such projects


1. Introduction to Marketing Analytics
  • A brief statistical review
  • A brief principles of consumer behavior and marketing strategy
  • What is an insight?
  • Introduction to R

2. Dependent variable techniques
  • Regression analysis
  • Classification

3. Inter-relationship techniques
  • Cluster analysis
  • Principal component anlaysis

4. Big data and big data analysis
  • What is big data?
  • Introduction to machine learning

5. Final Project
  • Research design
  • Data collection
  • Reporting results

Required prerequisite knowledge


Recommended previous knowledge

BØK104 Statistics and social science methodology
Students are expected to have completed compulsory levels of mathematics, statistics and economics for bachelor students in business.  


Term paper in group and individual digital written exam
Weight Duration Marks Aid
Term paper in group45/1001 A - FAll.
Individual digital written exam55/1004 hoursA - F
1. The term paper (45%): work in group of 3-4 students. Due at the end of the term.
There is no possibility of repeat (resit) exam for the group project.
3. Individual digital written exam (55%)
All the assessments are given and responded to in English.

Coursework requirements

Assignments, Attendance
The weekly assignments are given in forms of individually completed quizzes or group work. Groups are assigned by the instructor. All assignments are mandatory, and students need to "pass" all the assignments in order to write the term paper.
Group work is an important aspect of this course and integrated into the course meetings. Thus, students are required to attend 80% of the course meetings during the semester. Those who cannot meet this requirement should not take this course.

Course teacher(s)

Course coordinator
Yuko Onozaka

Method of work

The course will include a combination of lectures, group work sessions, mandatory assignments, and self-study. Students are expected to prepare for the lectures by reading the part of the curriculum that will be covered in each particular lecture.
The total work load in this course is estimated to be 280 hours.
Lectures and group work sessions : 66 hours (6 hours x 11 weeks)
Weekly assignments : 80 hours
Self-study : 80 hours
Final project: 50 hours
Final written exam: 4 hours

Overlapping courses

Course Reduction (SP)
Research methods in the social sciences (BRH220_1) 10

Open to

Business Administration - Bachelor's Degree Programme
Business Administration - Master of Science (5 years)
Exchange programmes at UIS Business School

Course assessment

Students will have the opportunity to give feedback on the course first in an early dialogue, and then in a written course evaluation at the end of the course.


Link to reading list

This is the study programme for 2020/2021.

Sist oppdatert: 05.08.2020