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 |
|
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 |
|
Last updated on
27-08-2020
|
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 of the Programme
Regulations): 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