OPM 560: Business Analytics: Introduction to Operations Analytics with Python
Contents
Business Analytics helps to optimize decisions for the design and management of operations systems and production processes. This course introduces into the programming language Python to implement selected concepts and methods from prescriptive and predictive analytics. They will be applied to capacity management and operations planning.
We apply descriptive analytics to quantify and visualize all three dimensions of variability, as introduced in OPM 561. For predictive analytics, we introduce data sampling and perform sensitivity analysis to understand the impact of stochastic variability. For prescriptive analytics, linear and mixed integer optimization models are implemented and solved numerically. During the course, the students will work on several case studies and assignments (individual and in groups).
Learning outcomes
Students will learn
- basics in Programming with Python.
- how to numerically analyze capacity planning and operations scheduling problems.
- how to use Python to implement and solve models from predictive and descriptive analytics with standard packages.
- how to deal with the complexity of real-world problems and how to perform sensitivity analysis in order to obtain useful managerial insights.
Necessary prerequisites
Successful completion of the course OPM 561 is required.
Recommended prerequisites
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| Forms of teaching and learning | Contact hours | Independent study time |
|---|---|---|
| Lecture with intergrated exercise | 2 SWS | 9 SWS |
| ECTS credits | 4 |
| Graded | yes |
| Workload | 120h |
| Language | English |
| Form of assessment | 70% assignments (individual and in groups) + 30% programming exam |
| Restricted admission | yes |
| Further information | – |
Examiner Performing lecturer |  | Prof. Dr. Raik Stolletz Prof. Dr. Raik Stolletz |
| Frequency of offering | Spring semester |
| Duration of module | 1 semester |
| Range of application | M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ. |
| Preliminary course work | – |
| Literature |
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| Course outline | Please note that this outline will be subject to changes: Get started with Python
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