English   Danish

2025/2026  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
Study Board for Service and Markets
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 01/07/2025

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 four assignments in total, including online quizzes and/or in-workshop tasks. To be eligible for the exam, students must receive 'approval' for at least three homework assignments.

The assignment format include online quizzes and/or in-workshop tasks.


NOTE:
The compulsory activities are not an exam but are assessed as approved/not approved. Three compulsory activities must be approved for the student to be eligible for the exam.

No extra tasks will be set before the regular exam for students who have been ill or have attempted all compulsory activities. Students automatically have extra attempts, since there are four assignments and only three need to be approved.

Failing to have three assignments approved, students will lose exam attempts in the subjectWinning in the Digital Age: Applications in Data Analysis with R.

Read more about mandatory activities at my.cbs.dk.
Examination
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 written product. Additionally, the source code must be included as an appendix with the written product 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, analytics, 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.

 
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Students conduct independent research-like activities under supervision
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 26 hours
Workshops 12 hours
Lecture prep 84 hours
Workshop prep 44 hours
Exam 40 hours
Last updated on 01/07/2025