OPM 560: Business Analytics: Introduction to Operations Analytics with Python

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. Based on OPM 561, selected concepts and methods from prescriptive and predictive analytics are implemented and numerically assessed. They will be applied to support decisions in 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

Recommended prerequisites
Successful completion of the course OPM 561 is recommended. OPM 560 starts in the second half of the semester (directly when OPM 561 is finished).

Forms of teaching and learningContact hoursIndependent study time
Lecture with integrated exercise2 SWS9 SWS
Exercise class1 SWS5 SWS
ECTS credits6
Graded yes
Form of assessmentAssignments (70%, individual and in groups) and programming exam (30%, Bring Your Own Device)
Restricted admissionno
Further information
Performing lecturer
Prof. Dr. Raik Stolletz
Prof. Dr. Raik Stolletz
Frequency of offeringFall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL
Preliminary course work
  • Guttag, J. V. (2016). Introduction to computation and programming using Python: With application to understanding data. MIT Press.
  • McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.“.
  • Journal papers will be announced during the lecture.
Course outlinePlease note that this outline will be subject to changes:
Get started with Python
  • Simple types and operators
  • Branching programs and conditional statements
  • While loops
  • For loops and ranges
  • Python data structures (list, dict, etc.)
  • Functions
Descriptive analysis
  • Read and write datasets
  • Univariate and Bivariate analysis
  • Quantify & visualize variability in datasets
Predictive analytics
  • Analyzing functions and sensitivities
  • Digital twins and random numbers
Prescriptive analytics
  • Implementation of Optimization models
  • Design numerical studies