052411 VU Business Intelligence I (2024S)
Prüfungsimmanente Lehrveranstaltung
Labels
Diese Lehrveranstaltung wird im SS 2023 voraussichtlich nicht stattfinden können.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 12.02.2024 09:00 bis Do 22.02.2024 09:00
- Abmeldung bis Do 14.03.2024 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Freitag
08.03.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
15.03.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
22.03.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
12.04.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
19.04.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
N
Freitag
26.04.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
03.05.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
10.05.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
17.05.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
24.05.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
31.05.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
07.06.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
14.06.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
21.06.
11:30 - 14:45
Seminarraum 7, Währinger Straße 29 1.OG
Freitag
28.06.
11:30 - 13:00
Hörsaal 1, Währinger Straße 29 1.UG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
The grade is derived from the sum of the two parts (i.e. a maximum of 100 points in total):
* Part A: practical exercises submitted individually or in small groups (max. 40 points)
* Part B: written exam (no aids allowed, max. 60 points).
* Part A: practical exercises submitted individually or in small groups (max. 40 points)
* Part B: written exam (no aids allowed, max. 60 points).
Mindestanforderungen und Beurteilungsmaßstab
‣ Part A: 40% practical exercises
‣ Part B: 60% 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: 60% 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)
Prüfungsstoff
* Lecture (slides)
* Exercises (theoretical and practical)
* Exercises (theoretical and practical)
Literatur
* Lecture slides
* Rick Sherman: Business Intelligence Guidebook: From Data Integration to Analytics, Morgan Kaufmann (1st edition), 2014.
* Wil van der Aalst: Process Mining – Data Science in Action (2nd edition), Springer, 2016
* Rick Sherman: Business Intelligence Guidebook: From Data Integration to Analytics, Morgan Kaufmann (1st edition), 2014.
* Wil van der Aalst: Process Mining – Data Science in Action (2nd edition), Springer, 2016
Zuordnung im Vorlesungsverzeichnis
Module: BI BI1 BUS
Letzte Änderung: Mi 03.04.2024 12:45
The goal of this course is to familiarize you with and teach you how to apply foundational concepts, modeling and analysis techniques, and tools that allow you to gain insights into the operations of organizations in a data-driven manner.The content of the course consists of:
- Foundational concepts for business intelligence (BI)
- Architectures and modeling techniques for data preparation and integration in BI settings
- How to take a process-oriented view on organizational operations
- Data-driven analysis of organizational processes using process mining
- Using BI and process mining tools to analyze real-world dataStudents, attending the course, are expected to have knowledge in the following topics:
* Basic knowledge of Python 3Knowledge about data modeling (e.g., entity-relationship models) and process modeling (e.g., Petri nets or BPMN) is helpful but not mandatory.