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Pfad: VVZ SoSe 2024 > Verzeichnis der LV gegliedert nach Instituten und Abteilungen

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Nr. LV-Typ(en) LV-Titel
5821 PI Business Analytics (Applied Track) Präsenz-Modus
Anmeldung über LPIS
vom 19.02.2024 15:00 bis 25.02.2024 23:59
Abmeldung über LPIS
vom 19.02.2024 15:00 bis 09.03.2024 23:59

LV-Leiter/in Assoz.Prof PD Florian Szücs, Ph.D., Ulrich Wohak, M.Sc.M.A.
Planpunkte Master Business Analytics
Semesterstunden 2
Unterrichtssprache Englisch

Termine
Di, 12.03.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 19.03.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 09.04.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 16.04.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 23.04.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 30.04.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 07.05.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 14.05.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 21.05.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 28.05.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 04.06.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 11.06.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 18.06.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Di, 25.06.2024 16:30-18:00 Uhr TC.3.06 (Lageplan)
Termindownload (ical) | Termine abonnieren

Weitere Informationen https://learn.wu.ac.at/vvz/24s/5821

Kontakt:
The instructors are available after class. Individual meetings with students can be arranged upon demand.
Inhalte der LV:

The course provides an introduction to business analytics for students with a background in economics. The following subjects are covered:

1) Regression methods familiar from econometrics classes are reviewed and applied to the analysis of selected business problems.

2) The analyst's toolbox is augmented by new methods such as machine learning and text mining.

3) The new tools and concepts are applied to a range of business problems.

4) In the final three units practitioners present actual business cases and discuss with students the use of data analysis in the private sector.

Lernergebnisse (Learning Outcomes):

Students acquire skills

- to adapt econometric models for the analysis of business problems;

- to use machine learning methods and data mining methods;

- to set up a project for analyzing a business problem;

- to apply STATA and R for business analytics;

- to understand the use of business analytics within the context of actual business cases.

 
 

 

 

Regelung zur Anwesenheit:

The attendance requirement is met if a student takes part in at least 80 percent of classes.

 
 

 

 

Lehr-/Lerndesign:

The courses relies upon a mix of teaching methods:

- Instructors present basic concepts and methods.

- Students work on problems to develop further their analytical skills.

- Research papers on business problems are discussed in class.

- Discussions of business cases with practitioners help to further widen and deepen the understanding of business analytics.

Throughout the course the emphasis is on applications of concepts and tools. Students may gain a deeper understanding of statisical foundations in the specialization "Data Science and Machine Learning" offered in the coming fall.

 

Leistung(en) für eine Beurteilung:

50 %: Coding Project

35 %: Essay on Research Paper

15 %: Class Participation

5 %: Bonus Points - Prediction Contest

 
 

 

 

Zuletzt bearbeitet: 13.11.2023 11:45

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