Business Analytics
Brief description
- Data, information, scales, and scale levels
- Basic mathematical/statistical methods
- Correct selection of methods for defined problems from practice and research
- Essential statistical procedures from descriptive statistics, probability theory and inductive statistics (e. g. correlation, regression, T-test or ANOVA)
- Conducting quantitative surveys (research conception, questionnaire conception, results analysis with a statistics programme)
- Getting to know and practising the use of common software products for data processing and data analysis (e. g. MS Excel, SPSS, PSPP)
- Practical exercises with freely available data sets: evaluations and interpretation of results
Mode of delivery
face to face
Type
compulsory
Recommended or required reading and other learning resources/tools
Braunecker, C. (2021): How to do Statistik und SPSS: Eine Gebrauchsanleitung. Uni-Taschenbücher GmbH.
Braunecker, C. (2021): How to do empirische Sozialforschung: Eine Gebrauchsanleitung, Uni-Taschenbücher GmbH
Hug, T., Poscheschnik, G. (2020): Empirisch forschen: Die Planung und Umsetzung von Projekten im Studium, Uni-Taschenbücher GmbH.
Chopra, S., & Meindl, P. (2016): Supply chain management: Strategy, planning, and operation (Chapter 3)
Planned learning activities and teaching methods
Input, blended learning, software-supported exercises, self-study phases
Assessment methods and criteria
Written final exam 70% (open questions)
Continuous assessment (30 %): Individual and group work, presentations, assessed partial examinations
Content criteria: Degree of problem identification and problem characterisation, complexity of solutions in terms of technical and methodological competence.
Formal criteria: Comprehensiveness of answers, correct use of technical terms, and independent presentation of results.
Prerequisites and co-requisites
None
Infos
Degree programme
Logistics & Strategic Management (Master)
Cycle
Master
ECTS Credits
3.00
Language of instruction
German
Curriculum
Part-Time
Academic year
2025
Semester
1 WS
Incoming
No
Learning outcome
After successful completion of the course, students can
- name and explain different types of data and information (scales) (2),
- explain the different ways of processing these scales (2),
- explain and apply different measures for the interpretation of data, especially positional dimensions, measures of variability,
- explain and apply basic statistical scientific methods for quantitative data analysis, e. g. test statistics, inferential statistics (3),
- know and explain the limits of applicability of evaluation methods (2),
- explain and apply qualitative and especially quantitative methods for collecting data and information (3),
- apply common software products for the analysis of data (3),
- explain the importance of generic data analysis for entrepreneurial problems and decision making (2),
- evaluate (analyse) and correctly interpret data from real business situations, e. g. market research, controlling or quality management (4),
- correctly apply qualitative and quantitative methods for master thesis research work (3).
Course code
1392-21-01-BB-DE-08