| Learning objectives |
- Understand and deploy techniques for exploring and analyzing
structured data
- Understand and deploy basic machine learning techniques for
classification and regression
- Understand and deploy techniques for visualizing and presenting
results of data analytics
- Demonstrate an analytical understanding of business, societal,
and ethical issues in the application of data analysis
techniques
|
| Examination |
|
Business Data
Analytics, Quantitative Methods and
Visualization:
|
| Exam
ECTS |
7,5 |
| Examination form |
Oral exam based on written product
In order to participate in the oral exam, the written product
must be handed in before the oral exam; by the set deadline. The
grade is based on an overall assessment of the written product and
the individual oral performance, see also the rules about
examination forms in the programme regulations. |
| Individual or group exam |
Oral group exam based on written group
product |
| Number of people in the group |
2-4 |
| Size of written product |
Max. 15 pages |
| Assignment type |
Written assignment |
| Duration |
Written product to be submitted on specified date and
time.
15 min. per student, including examiners' discussion of grade,
and informing plus explaining the grade |
| Grading scale |
7-point grading scale |
| Examiner(s) |
Internal examiner and second internal
examiner |
| Exam period |
Summer |
| Make-up exam/re-exam |
Same examination form as the ordinary exam
|
Description of the exam
procedure
If the student has participated in the written group project for
the ordinary exam, but didn't attend the oral exam, the
re-examination is conducted on the basis of the group project that
has already been handed in.
However, a copy of the project for the ordinary exam MUST be handed
in for the re-exam within a specified time.
If the student has participated in the written group project for
the ordinary exam, but not passed the oral exam, the re-exam is
normally conducted on the basis of the project that has already
been handed in. However, the student may choose to hand in a new,
individual project within a specified time.
NB! The student must clearly state at the frontpage of the project,
if the product is the IDENTICAL to project handed in for the
ordinary exam, or if the student has chosen to hand in a NEW
PROJECT.
If the student has not submitted the written group project for the
ordinary exam, the student may participate in the oral
re-examination, if the student hands in an individual project
within a specified time.”
|
|
| Course content, structure and pedagogical
approach |
|
This course is designed to equip students with practical
knowledge of tools and techniques for the exploration, analysis and
visualization of data in business. It also deals with conceptual,
societal and ethical issues associated with these
techniques.
The course has a blended format, with some
lectures presented online, together with associated online
activities. In addition, there will be regular hands-on
lab sessions. The course includes an independently chosen
project, which will take the form of a business case
analysis. Students will select a dataset, to which they apply
data science techniques, building relevant models and
assessing them from a business and data science perspective.
The course will cover the following main topic areas:
- Basic techniques for analysis of structured data, including use
of query languages
- Basic machine learning tools and techniques, including
classification and regression, as well as unsupervised methods
such as clustering
- Techniques for visualization and presentation of the results of
data analysis
- Conceptual, societal and ethical issues with business data
analytics
|
| Description of the teaching methods |
| A mixture of face to face and online lectures,
other online activities such as quizzes, group work, and practical
exercises in hands-on sessions |
| Feedback during the teaching period |
Students submit result of hands-on exercises each
week, and they receive detailed written feedback on their
submissions before the following session. Students also receive
informal feedback on preliminary plans for a course project.
|
| Student workload |
| Lectures |
30 hours |
| Readings and class preparation |
116 hours |
| Exam Project and Preparation for Exam |
60 hours |
|
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