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