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2020/2021  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 100
Study board
BSc in Digital Management
Course coordinator
  • Qiqi Jiang - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Management
  • Statistics and quantitative methods
Teaching methods
  • Online teaching
Last updated on 27-08-2020

Relevant links

Learning objectives
  • Understand fundamental knowledge in 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 s. 13): 2
Compulsory home assignments
There are three home assignment. Those who (at least) pass two home assignment are entitled to submit the final write-up.
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. 5 pages
Assignment type Case based assignment
Duration 24 hours to prepare
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The exam will be 24-hour assignment. Students need to answer the case-based questions by building up models, analyzing the given dataset, and interpreting your findings.

Course content, structure and pedagogical approach

In the digital age, organizations are inundated with massive data. Strategic use of the data to generate new values play a pivotal role in shaping the business performance. Thus, statistical and computational tools are valued in the digital economy. This course is designed to equip students both practical skills and theoretical interpretation of different statistical and computational models and tools in R. After taking this course, the students can master not only analytical skills such as programming and statistics, but also the capability of building interactive application to present the analysis results. In addition, the students can also master the professional skills such as communication, teamwork, problem solving, and critical thinking.

The course includes both lecturing and hands-on sessions. The students are also encouraged to participate in a series of online activities. Each lecture has its own thematic topic and sample data. There are three home assignment. Those who (at least) pass two home assignment are entitled to submit the final write-up.

Description of the teaching methods
The course is blended-formatted, including lecturing, hands-on sessions, and some online activities.
Feedback during the teaching period
The students can get immediate feedback from the hands-on sessions. In addition, the students can also get feedback from their submitted homework.
Student workload
Lectures 24 hours
Workshops 24 hours
Lecture prep 80 hours
Workshop prep 38 hours
Exam 40 hours
Last updated on 27-08-2020