WIW74000 – Information Technologies

Module
Information Technologies
Information Technologies
Module number
WIW74000
Version: 1
Faculty
Business Administration
Level
Bachelor
Duration
1 Semester
Semester
Summer semester
Module supervisor

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

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

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

English
in "Information Technologies"
ECTS credits

5.00 credits

Workload

150 hours

Courses

4.00 SCH (4.00 SCH Lecture with integrated exercise / seminar-lecture)
Self-study time

90.00 hours
60.00 hours Self-study - Information Technologies
30.00 hours Beleg und Vortragsausarbeitung - Information Technologies
Pre-examination(s)
None
Examination(s)

alternative Prüfungsleistung - Beleg mit Vortrag
Module examination | Weighting: 100% | wird in englischer Sprache abgenommen
in "Information Technologies"
Media type
No information
Instruction content/structure
  • Basics and Architectures in the Internet of Things (IoT)
  • Introduction to the digital transformation of businesses
  • Concepts and methods of computer-aided data analysis
    and their applications
  • Data and its pre-processing
  • Case studies for data analysis in practical applications
  • Application of Big Data solutions
Qualification objectives

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.
Special admission requirements

keine
Recommended prerequisites
No information
Continuation options
No information
Literature
  • 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
Notes
No information
Assignment to curriculum

470 Green Engineering and Sustainable Management - Bachelor 2022 Vollzeit

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