Maßgeschneiderte E-/Online-Veranstaltung zu „Data Literacy“ im Masterstudium
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|11.10 - 08.11|
Klausur 1 (04.11.2021): Anmeldezeitraum: 07.10.2021 - 24.10.2021, Abmeldefrist: 28.10.2021
Klausur 2 (02.12. 2021): Anmeldezeitraum: 11.11.2021 - 25.11.2021, Abmeldefrist : 28.11.2021
|Datum der Klausureinsicht : Februar 3rd 2022 (03.02.2022) Zeitraum: 15:00 Uhr – 17:00 Uhr Raum: (WIWI) R 301 Anmeldezeitraum: 20.01.2022 - 30.01.2022||Registration form for exam review|
2 SWS (150h mainly own work, possibly some hours of attendance)
Calculation basis: 15 weeks in a semester, including an examination week; each SWS corresponds to 60 minutes.
|Background||Basic knowledge in quantitative methods at the level of a management-oriented or economics-oriented bachelor’s degree|
According to § 3 of the study and examination regulations for the Master's program in Business Administration
Against the background of continuous advances in digital technologies, competencies in Data Analytics and Data-Driven Decision Making, summarized as Data Literacy and the fundamentals of Mathematics and Statistics (Mathematical Literacy) form a fundamental framework of modern management. This innovative online course allows its participants to refresh and strengthen these competencies employing an highly individual learning scheme.
The course covers four subject areas.:
1) Fundamentals of Mathematics:
• Sums, products, sets, linear equations, inequalities
• Calculus (functions, limits, derivatives and integration)
• Linear algebra (matrix algebra and systems of linear equations)
2) Fundamentals of Statistics
• Random variables and stochastic modeling
• Estimation and test theory
• Regression modeling
3) Fundamentals of Management Science
• Modeling of optimization problems
• Introduction to algorithms, heuristics and metaheuristics
• Linear programming
4) Fundamentals of Empirical Research Methods
• Business research process
• Primary and secondary data collection methods
• Hypothesis testing
- The main objective of this course is to build up and strengthen the core competencies of mathematical literacy and data literacy
- The aim of the course is to equip students with a solid mathematical and analytical knowledge to successfully study questions and address challenges in the field of modern digital Management, like Data Analytics or Data-Driven Decision Making.
- The course prepares students for advanced courses in the field of data literacy offered in the Master’s program
- E-learning/online course (possibly accompanied by some supporting teaching sessions)
- Intensive block course (~4 weeks) with individual learning organization
- Participants take a mandatory online placement test at the beginning of the course to assess their current knowledge and competencies.
- Based on the test results, participants will get their individual learning objectives for the course.
- After the test, participants get access to a structured, comprehensive e-library with teaching videos, quizzes, tests, forums, scripts and other online materials.
- Participants learn with the provided material in a flexible manner and according to their individual needs to achieve the qualification objectives.
- Portfolio examination. The final grade depends on the successful completion of e-assessments qualifying in all four subject areas of the course.
- Alwan, L. C., Craig, B. A., and McCabe G. P. (2020). The Practice of Statistics for Business and Economics. 5th edition. Macmillan International Higher Education: New York.
- Bertsimas, D., and Tsitsiklis, J. N. (1997). Introduction to Linear Optimization. Athena Scientific: Massachussets.
- Gillard, J. (2020) A First Course in Statistical Inference. Springer: Cham.
- Luderer, B., Nollau, V., and Vetters, K. (2007). Mathematical Formulas for Economists. 3rd edition. Springer: Berlin, Germany.
- Navarro, D. (2018) Learning Statistics with R, open.umn.edu/opentextbooks/textbooks/559
- Quinlan, C., Babin, B., Carr, J. Griffin, M., and Zikmund, W. G. (2019). Business Research Methods. 2nd edition. South-Western, Cengage Learning: Andover, UK.
- Simon, C.P., and Blume, L. (1994). Mathematics for Economists. WW Norton & Co: London, UK.
- Winston, W. (2003). Operations Research: Applications and Algorithms. Brooks/Cole: Belmont