Prescriptive analytics is concerned with using quantitative tools to turn data into managerial and operational decisions, in both deterministic settings and under risk. This unit introduces mathematical optimisation modelling, with applications to problems in management, logistics, economics, science and engineering. Students will learn techniques for rigorously formulating complex decision-making problems as mathematical models, state-of-the-art computational tools to solve the models, how to incorporate measures of risk into models, and how to interpret outputs of models in the relevant decision-making context. It is expected that students have a good understanding of fundamental data analytics concepts such as vectors, matrices, probability, and the Python programming language.
Unit details and rules
Academic unit | Business Analytics |
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Credit points | 6 |
Prerequisites
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ECMT5001 or QBUS5001 |
Corequisites
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BUSS6002 |
Prohibitions
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None |
Assumed knowledge
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Vectors, matrices, probability, Python |
Available to study abroad and exchange students | Yes |
Teaching staff
Coordinator | Nam Ho-Nguyen, nam.ho-nguyen@sydney.edu.au |
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Lecturer(s) | Nam Ho-Nguyen, nam.ho-nguyen@sydney.edu.au |