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39720 Vorlesung: Fundamentals of Business Analytics (SoSe 22)
Lehrende
Zeiten
Termine am Montag. 11.04.22 09:30 - 10:30Ort
(Zoom (access via Stud.Ip))Erster Termin
Mo., 11.04.2022 09:30 - 10:30 Uhr, Ort: (Zoom (access via Stud.Ip))ECTS
5SWS
5
Beschreibung
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
Heimateinrichtung
Lehrstuhl für Betriebswirtschaftslehre mit Schwerpunkt Management Science / Operations and Supply Chain ManagementBeteiligte Einrichtungen
Anrechenbarkeit
Studienangebote in anderen Sprachen > Studienangebot in englischer Sprache
Schwerpunkte der Universität > Digitalisierung / KI
Alte Zuordnungen (bis WS 2022/23) > Wirtschaftswissenschaftliche Fakultät > Master Business Administration > Version 1 > Gesamtkonto MR BA > Methoden > 261003 | Fundamentals of Business Analytics
Teilnehmende
M.Sc.BAVoraussetzungen
According to § 3 of the study and examination regulations for the Master's program in Business Administration Basic knowledge in quantitative methods at the level of a management-oriented or economics-oriented bachelor’s degreeLernorganisation
- 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.Leistungsnachweis
Portfolio examination. The final grade depends on the successful completion of e-assessments qualifying in all four subject areas of the course.Literatur
Key textbooks:
• 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
• 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
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