Data Science & Intelligent Analytics PT
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Data Science for Business & Commerce

level of course unit

Master course

Learning outcomes of course unit

Students are familiar with the usage areas of data acquisition, data storage, data analysis and data usage within the context of business-related and digital-commerce applications. They understand the special challenges of this usage area and are familiar with established best practice methods. Furthermore, they are able to independently design and implement data-based applications in this area while taking domain-specific requirements into account.

prerequisites and co-requisites

not specified

course contents

Students acquire detailed knowledge of the techniques and tools of data science in the area of business and commerce, specifically in:

- Business intelligence and management information systems (e.g. dash-boards)
- Key figure systems and data structures
- Forensic data analysis for fraud detection
- Process mining for procedural optimization/illustration
- Recommender systems (user/item/content-based collaborative filtering)
- Customer profile analysis (e.g. lead scoring, customer lifetime value, etc.)

The purpose of this course is to give students special insight into other areas of data processing and expand their problem-solving horizon

recommended or required reading

- Cady, F. (2017) The Data Science Handbook. 2. Auflage, Wiley, Hoboken (ISBN: 978-1119092940).
- Meier, A.; Stormer, H. (2012) eBusiness & eCommerce: Management der digitalen Wertschöpfungskette. 3. Auflage, Springer, Berlin (ISBN: 978-3-642-29801-1).
- Tamm, G. (2003) Konzepte in eCommerce Anwendungen. 1. Auflage, SPC TEIA Lehrbuch, Kelkheim (ISBN: 978-3935539661).

assessment methods and criteria

Seminar thesis or final examination

language of instruction

English

number of ECTS credits allocated

3

course-hours-per-week (chw)

2

planned learning activities and teaching methods

Lecture with discussion
Group work
Performing exercise tasks

semester/trimester when the course unit is delivered

3

name of lecturer(s)

Head of studies

year of study

2

recommended optional program components

N.A.

course unit code

VT.2

type of course unit

ILV

mode of delivery

In-class course

work placement(s)

N.A.

Kontaktpersonen

Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
Karsten.Boehmfh-kufstein.ac.at
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Förderer

Mit Unterstützung von Bund, Land und Europäischer Union: