| 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.
|
| 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 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 |
|
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