Upon completion of this course, students should be familiar with:
- fundamental data analysis techniques, such as various regression models and segmentation tools
- writing a simple R code for fundamental statistical analysis
- different types of research designs
- different sampling methods, data collection methods and measurement issues
Upon completion of this course, students will be able to:
- obtain and visualize basic descriptive features of data
- 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
- understand, evaluate and utilize the results from other research studies
Upon completion of this course, students will have acquired and developed an understanding of the fundamentals thinking around marketing analytics, and how to gain marketing insights using data and statistical tools.
- A brief statistical review
- A brief principles of consumer behavior and marketing strategy
- What is an insight?
- Introduction to R
2. Dependent variable techniques
- What drives demand?
- Regression analysis (ordinary least squares, logistic regression, count data regression, panel regression)
3. Inter-relationship techniques
- What does my customer market look like?
- Segmentation analysis (clustering, latent class analysis, random forest)
4. Big data and big data analysis
- What is big data?
- How to analyze big data?
5. Final Project
- Research design
- Data collection
- Reporting results
Required prerequisite knowledge
Recommended previous knowledge
|Term paper in group||1/2||1||A - F||All. |
|Oral exam||1/2||A - F|
The oral exam is graded individually. There is no possibility of repeat exam for the group project.
The term paper and the oral exam will be given and responded to in English.
Participation is an important aspect of this course. Thus, students are required to attend all lectures and sessions during the semester. Those who cannot attend lectures should not take this course.
- Course coordinator
- Yuko Onozaka
Method of work
The total work load in this course is estimated to be 250-300 hours.
Business Administration - Master of Science (5 years)
Exchange programmes at UIS Business School
Optional (supplementary): "R for Marketing Research and Analytics" by Chris Chapman and Elea McDonnell Feit. Springer, 2015.
Sist oppdatert: 17.06.2019