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2023/2024  KAN-CDIBV1002U  Applying Data Analytics in Digital Business

English Title
Applying Data Analytics in Digital Business

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 100
Study board
Master of Science (MSc) in Business Administration and Digital Business
Course coordinator
  • Qiqi Jiang - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 01-02-2023

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Understand the strategic value of quantitative methods that help to solve business problems using data.
  • Be able to select appropriate tools and methods for data collection and analysis to answer specific managerial questions.
  • Be able to translate results from quantitative analysis into business insights.
  • Be able to communicate insights from quantitative analysis clearly and persuasively to a business audience in written and verbal form.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 3
Compulsory home assignments
There are 3 mandatory activities through the semester. For each of the main course sections, (1) survey research, (2) experimental research, and (3) prediction models, there will be a multiple choice test at the end of the section.

Each student has get 2 activities approved in order to go to the ordinary exam. There will not be any extra attempts provided to the students before the ordinary exam.
If a student cannot participate due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (10 pages) which will cover 2 mandatory assignments.
Applying Data Analytics in Digital Business:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Report
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The students need to solve some scenario-based problems using the data analytics methods covered in the course. For example, a scenario of bike sharing demand will be given along with the related dataset, and students need to provide solutions for problems such as pricing strategy, identifying the factors that can boost the demand, and etc.

Course content, structure and pedagogical approach

Strategic use of data can help organizations to generate business insights that provide objective advice relating to the strategy, structure, management and operations of an organization. The digitalization of industries has given firms easy access to large amounts of data that can be used to derive valuable business insights. Especially, small and medium sized firms can benefit from the increased access to relevant data to generate valuable business insights without significant investment. For instances, large scale online surveys provide detailed and comprehensive insights on consumers’ and citizens’ perceptions. E-commerce leaders are conducting a plethora of experiments to find the best performing design of their mobile app and website. Music streaming providers use data on our listening behavior to accurately predict our music preferences. For companies, the question is how to collect and analyze relevant data to gain the valuable business insights.


When analyzed rigorously, data offers tremendous business potential. This is why firms are looking for university graduates possessing the skills to collect, analyze, and interpret quantitative data. Especially employees at the intersection of business and analytics can provide significant value to companies. These employees possess fundamental knowledge of both business and analytics that helps firms to steer their analytics efforts into strategically valuable directions. The aim of this course is to understand the business value of data and become able to unlock this value for businesses.


This course will enable you to learn how to apply different methods to collect and analyze data to inform business decisions. Deploying rigorous methods is the cornerstone to answer important business questions. We will use statistical analysis software and interpret the business implications of our empirical findings. We will train you in how to ask research questions and how to answer them using quantitative research methodologies. To achieve our learning objectives, you will work on interesting hands-on projects in a digital context applying survey methodology, experimentation, and predictive analysis.


Importantly, this course does not require any pre-experience in statistics but rather your motivation and interest to acquire these valuable skills. This course can also help you to conduct quantitative research for your Master thesis project.

Description of the teaching methods
Online lecture with pre-recorded video
On-campus tutorial workshop
Feedback during the teaching period
Students will receive feedback on their performance and how to improve during:
* Exercises in lectures that are being prepared by students and discussed in class. The teachers will give feedback on the solutions suggested by students.
* Group presentations that are being conducted in the workshop sessions. Students will be working on activities related to material covered in the lectures and present their work. Students will then receive feedback from peers as well as the teachers.
* Individual multiple choice tests at the end of each section. Students will get feedback when conducting the mandatory individual multiple choice tests by comparing the answers they provided with the correct answers.
Student workload
Lectures 24 hours
Workshops 24 hours
Lecture preparation and reading 80 hours
Workshop preparation 38 hours
Final Assignment/Examination 40 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any material. Here are some examples.

*Almenberg, J., & Dreber, A. (2015). Gender, stock market participation and financial literacy. Economics Letters, 137, 140-142.

*Bleier, A., and Eisenbeiss, M. 2015. The Importance of Trust for Personalized Online Advertising. Journal of Retailing, 91(3), pp. 390–409.

*Cox. (2017). Exploratory Data Analysis: What Data Do I Have? In Translating Statistics to Make Decisions (pp. 47–74). Apress. https:/​/​doi.org/​10.1007/​978-1-4842-2256-0_3

*Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT sloan management review, 55(2), pp. 1-12.

*Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1, pp.104-121.

*Hoehle, H., Aloysius, J.A., Goodarzi, S., and Venkatesh,V. (2019). A nomological network of customers’ privacy perceptions: linking artifact design to shopping efficiency, European Journal of Information Systems, 28:1, pp. 91-113

*Jung, E. Y., Baek, C., & Lee, J. D. (2012). Product survival analysis for the App Store. Marketing Letters23(4), 929-941.

*Lai, Y. L., Lin, F. J., & Lin, Y. H. (2015). Factors affecting firm's R&D investment decisions. Journal of Business Research, 68(4), 840-844.

*List, J.A., 2011. Why Economists Should Conduct Field Experiments and 14 Tips for Pulling One Off. Journal of Economic Perspectives, 25(3), pp.3-16.

*Prentice, C., Nguyen, M. (2021). Robotic service quality – Scale development and validation. Journal of Retailing and Consumer Services, Vol: 62, pp.1-7.

*Schuetz, S.W., Sykes, T.A. and Venkatesh, V. (2021). Combating COVID-19 fake news on social media through fact checking: antecedents and consequences, European Journal of Information Systems, 30:4, pp. 376-388.

*Shmueli, G., & Koppius, O. R. 2011. Predictive analytics in information systems research. MIS Quarterly, 35(3), pp.553-572.

*Tangi L., Janssen M., Benedetti M., Noci G. (2020). Barriers and Drivers of Digital Transformation in Public Organizations: Results from a Survey in the Netherlands. In: *Viale Pereira G. et al. (eds) Electronic Government, pp. 42-56.

Last updated on 01-02-2023