052411 VU Business Intelligence 1 (2022S)
Continuous assessment of course work
Labels
MIXED
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 14.02.2022 09:00 to Th 24.02.2022 10:00
- Deregistration possible until Mo 14.03.2022 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Kickoff meeting (online): 02.03. 09:45
Link: https://bbb.cs.univie.ac.at/b/mar-9nf-2s4-hhh
Wednesday
02.03.
09:45 - 13:00
Digital
Wednesday
09.03.
09:45 - 13:00
Digital
Wednesday
16.03.
09:45 - 13:00
Digital
Wednesday
23.03.
09:45 - 13:00
Digital
Wednesday
30.03.
09:45 - 13:00
Digital
Wednesday
06.04.
09:45 - 13:00
Digital
Wednesday
27.04.
08:00 - 11:15
Digital
Wednesday
04.05.
08:00 - 11:15
Digital
Wednesday
11.05.
08:00 - 11:15
Digital
Wednesday
18.05.
08:00 - 11:15
Digital
Wednesday
25.05.
08:00 - 11:15
Digital
Wednesday
01.06.
08:00 - 11:15
Digital
Wednesday
08.06.
09:45 - 13:00
Digital
Wednesday
15.06.
09:45 - 13:00
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
22.06.
09:45 - 13:00
Digital
Wednesday
29.06.
11:30 - 14:45
Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 10, Währinger Straße 29 2.OG
Seminarraum 9, Währinger Straße 29 2.OG
Seminarraum 10, Währinger Straße 29 2.OG
Seminarraum 9, Währinger Straße 29 2.OG
Information
Aims, contents and method of the course
Assessment and permitted materials
The grade is derived from the sum of the two parts (i.e. a maximum of 100 points in total):
* Part A: theoretical and practical exercises submitted individually (max. 55 points)
* Part B: written exam (no aids allowed, max. 45 points).
* Part A: theoretical and practical exercises submitted individually (max. 55 points)
* Part B: written exam (no aids allowed, max. 45 points).
Minimum requirements and assessment criteria
‣ Part A: 55% theoretical and practical exercises
‣ Part B: 45% written examOverall at least 50%of the points need to be achieved.The grade is calculated from the total points as follows:
>= 87,5% very good (1)
>= 75% good (2)
>= 62,5% satisfactory (3)
>= 50% sufficient (4)
< 50% not sufficient (5)
‣ Part B: 45% written examOverall at least 50%of the points need to be achieved.The grade is calculated from the total points as follows:
>= 87,5% very good (1)
>= 75% good (2)
>= 62,5% satisfactory (3)
>= 50% sufficient (4)
< 50% not sufficient (5)
Examination topics
* Lecture (slides)
* Exercises (theoretical and practical)
* Exercises (theoretical and practical)
Reading list
* Lecture slides
* W. Grossmann, S. Rinderle-Ma: Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg, doi: 10.1007/978-3-662-46531-8 (2015)
* Friedman, J., Hastie, T., Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics
* W. Grossmann, S. Rinderle-Ma: Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg, doi: 10.1007/978-3-662-46531-8 (2015)
* Friedman, J., Hastie, T., Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics
Association in the course directory
Module: BI BI1 BUS
Last modified: Th 11.05.2023 11:27
* Introduction, familiarization and application of methods and tools for business intelligence
* Getting to know and apply analysis methods of process and cross-sectional dataThe content of the lecture consists of:
* Methodology and modeling techniques in business intelligence
* Data models in business intelligence and data quality
* Analysis of process data and process discovery (process mining)
* Social network analysis
* Text mining and Opinion Mining
* Business intelligence tools to apply business intelligence techniquesStudents, attending the course, are expected to have knowledge in the following topics:
* Knowledge of Python3.6+ (especially text processing and data analytics libraries)