WIW34170 – Research Methods in Data Science

Module
Research Methods in Data Science
Research Methods in Data Science
Module number
WIW34170
Version: 1
Faculty
Business Administration
Level
Master
Duration
1 Semester
Semester
Summer and Winter semester
Module supervisor

Prof. Dr. Christian-Andreas Schumann
Christian.Schumann(at)fh-zwickau.de
Lecturer(s)

Lecturer of the Armenian State University of Economics (ASUE)

Course language(s)

English
in "Research Methods in Data Science"
ECTS credits

5.00 credits

Workload

150 hours

Courses

4.00 SCH (1.00 SCH Vorlesung | 3.00 SCH Internship)
Self-study time

90.00 hours

Pre-examination(s)

Participation
in "Research Methods in Data Science"
Examination(s)

alternative Prüfungsleistung - Laborarbeit
Module examination | Examination time: 90 min | Weighting: 100% | wird in englischer Sprache abgenommen
in "Research Methods in Data Science"
Media type
No information
Instruction content/structure
  • T-tests for comparison of means and interpret the results
  • within-group and between-group Variations
  • factor analysis in solution of economic issues,
  • agglomeration clusterization and K-means method,
  • logistic regression and discriminant analysis.
Qualification objectives

The aim of the subject is to develop detailed knowledge of the data research methods and their implementation issues.  Upon completion of the subject, students will be able to understand the purpose and procedures of data analysis, implement different analytical methods, interpret the received results.
Special admission requirements

keine
Recommended prerequisites

Basic knowledge of math and statistics
Continuation options
No information
Literature
  • Johnson, R.A. & D. W. Wichern (2012). Applied multivariate statistical analysis Sixth Edition, PHI, 2012.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed). Boston, MA: Pearson
  • Davison, A. (2008) Statistical Models, Cambridge University Press.
Notes

This subject will be studied at ASUE, Yerevan, Armenia as an elective one in the 2nd or 3rd semester.

Lecturer – Prof. Armen Ktoyan

Prerequisite(s) for examination admission:

  • Proof of participation

Exam:

  • laboratory task
Assignment to curriculum
No information
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