WIW64030 – Analytics for Data Driven Decisions

Modul
Analytics for Data Driven Decisions
Analytics for Data Driven Decisions
Modulnummer
WIW64030
Version: 1
Fakultät
Wirtschaftswissenschaften
Niveau
Master
Dauer
1 Semester
Turnus
Sommer- und Wintersemester
Modulverantwortliche/-r

Prof. Dr. Christoph Laroque
Christoph.Laroque(at)fh-zwickau.de

Prof. Dr. Susanne Bleich
Susanne.Bleich(at)fh-zwickau.de

Prof. Dr. Matthias Richter
M.Richter(at)fh-zwickau.de

Dozent/-in(nen)

N.N.

Lehrsprache(n)

Deutsch
in "Analytics for Data Driven Decisions"

Englisch
in "Analytics for Data Driven Decisions"

ECTS-Credits

5.00 Credits

Workload

150 Stunden

Lehrveranstaltungen

4.00 SWS (4.00 SWS Vorlesung mit integr. Übung / seminaristische Vorlesung)

Selbststudienzeit

90.00 Stunden
90.00 Stunden Selbststudium - Analytics for Data Driven Decisions

Prüfungsvorleistung(en)
Keine
Prüfungsleistung(en)

schriftliche Prüfungsleistung -
Modulprüfung | Prüfungsdauer: 90 min | Wichtung: 100%
in "Analytics for Data Driven Decisions"

Medienform
Keine Angabe
Lehrinhalte/Gliederung

Concepts and methods of computer-aided data analysis
and their applications in the context of business administration,
but especially

- Data and its pre-processing
- Visualization of information sets
- Special methods of data analysis, e.g. Statistical Data Analysis, Six Sigma Methods, Clustering & Data Mining
- Algorithms, methods and tools for mechanical
learning
- Case studies for data analysis in practical application
- Application of BigData solutions

Qualifikationsziele

The students acquire skills for understanding and for
the use of a wide variety of methods and concepts of the
computer-aided data analysis and its correct,
practical application. They are able to make observations and
structure information sets in such a way that their
essential patterns are recognizable and
business decisions can be made.

The students are able to make appropriate key performance indicators, figures and
methods for characterizing empirical data
and also calculate it for very large amounts of data. They
have mastered the essential concepts of graphical representations of
data that can be used for further analyses with the help of statistical
methods of machine learning by applying existing software solutions.

Sozial- und Selbstkompetenzen
Keine Angabe
Besondere Zulassungsvoraussetzung

keine

Empfohlene Voraussetzungen
Keine Angabe
Fortsetzungsmöglichkeiten
Keine Angabe
Literatur
  • Thomas A. Runkler: Data Analytics - Models and Algorithms for Intelligent Data Analysis, Springer Fachmedien Wiesbaden 2016, DOI: 10.1007/978-3-658-14075-5

  • Steven Finlay: Predictive Analytics, Data Mining and Big Data - Myths, Misconceptions and Methods, Palgrave Macmillan, a division of Macmillan Publishers Limited 2014, DOI: 10.1057/9781137379283

  • Sayan Mukhopadhyay: Advanced Data Analytics Using Python - With Machine Learning, Deep Learning and NLP Examples, Apress, Berkeley, CA, DOI: 10.1007/978-1-4842-3450-1

  • OECD (2013), Exploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by „Big Data“, OECD Digital Economy Papers, No. 222, OECD Publishing
  • Glass, R.: The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits, John Wiley & Sons, 2015
Hinweise
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