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2024/2025  BA-BDMAV2001U  Winning in the Digital Age: Applications in Data Analysis with R

English Title
Winning in the Digital Age: Applications in Data Analysis with R

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
BSc in Digital Management
Course coordinator
  • Qiqi Jiang - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Management
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 23-01-2024

Relevant links

Learning objectives
  • Gain a fundamental understanding of R programming
  • Understand and deploy relevant statistical models for analyzing complex dataset
  • Understand and develop critical inferential thinking
  • Understand and develop interactive applications to present analysis results
  • Demonstrate the business and societal value from data analysis results
Course prerequisites
The pre-experience in statistics and programming is not mandatory but recommended.
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 three homework assignments (multiple-choice quizzes) in total. To be eligible for the exam, students must receive 'approval' for at least two homework assignments.

Oral presentations etc.
There are two on-campus, hands-on workshops. Students must participate in at least one workshop to receive 'approval' for the exam.
Winning in the Digital Age: Applications in Data Analysis with R:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Students are required to submit the source codes used for conducting data analytics or analytical inquiries as an appendix.
Assignment type Written assignment
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter and Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Only those who meet the prerequisites for registering for the exam successfully are entitled to participate in the retake exam.
Description of the exam procedure

Students are required to submit a 10-page written product by a specified date. The submission should include the following components: 1) proposing a research question, 2) finding a dataset, 3) answering the research question by building models and analyzing the dataset, and 4) discussing the findings and implications. It's important to note that the data sample used in class cannot be reused for the exam write-up. Additionally, the source code must be included as an appendix with the write-up submission.


Course content, structure and pedagogical approach

Data is the new oil of the digital economy. Companies can strategically leverage data to generate new value and shape business performance. New AI tools, such as Large Language Models like ChatGPT, are developed based on extensive training data. To extract value from raw data, sophisticated statistical and computational models are essential.


This course is designed to equip students with both practical skills and theoretical understanding of various statistical and computational models and tools in R. State-of-the-art technologies, including machine learning algorithms, text mining, and artificial intelligence, will be covered. By taking this course, students can learn both the theory and application of analytical models. Additionally, students can build interactive applications to present analysis results and insights to a non-technical audience.


The course includes both lectures and hands-on sessions. Students are also encouraged to participate in a series of online activities. Each lecture focuses on a thematic topic and includes sample data. Two on-campus workshops provide students with hands-on tutorials.


Description of the teaching methods
The course includes pre-recorded videos for lectures and hands-on sessions, along with online activities. Additionally, there will be on-campus workshops.
Feedback during the teaching period
Students receive immediate feedback during online hands-on sessions. Additionally, they can also get feedback and hands-on experience during on-campus workshops.
Student workload
Lectures 24 hours
Workshops 24 hours
Lecture prep 80 hours
Workshop prep 38 hours
Exam 40 hours
Last updated on 23-01-2024