Automated Quality Analysis with Python

Automated Quality Analysis with Python (5 afternoons)

Are you also in the process of fully digitizing your quality controls? Quality controls can be automated on the basis of machine, process and sensor data. In this process, artificial intelligence (AI) algorithms replace the previous random inspection with a 100% digital inspection. In addition, digitizing the inspections automatically establishes 100% traceability and documentation. In perspective, this provides the basis for further process cost savings, such as an elimination of incoming material inspections.

The Python programming language and the included data science libraries provide you with modern and flexible analysis tools for your calculations. You link your proven quality analysis methods with AI-supported algorithms in one platform. This enables you to establish automated, real-time and sustainable quality assurance routines for your processes.

In this e-training, you will learn in a balanced mix of online training and virtual group phases, step by step through practical exercises, how to use Python for your data-based quality control. In addition to an introduction to the software, you will learn how to make established quality control methods more efficient using AI.

Your benefit

  • You will get an overview of Python's capabilities for processing and analyzing very large data streams.
  • You will be able to analyze and visualize data in an exploratory way and apply common statistical functions.
  • You will get an overview of how you can implement Six Sigma analyses with Python in the future.
  • You will know the most important techniques of feature engineering.
  • You will learn the practical use of selected algorithms of Artificial Intelligence and Machine Learning and train your own models.
  • You will deepen the theoretical knowledge in the form of practical exercises.

Who should attend

Quality experts from organizations in the processing and manufacturing industry as well as from suppliers, process owners, test planners, developers, people responsible for components, employees with sampling and approval responsibility.

Course Outline

  • Introduction to Python and relevant Python libraries
  • Techniques for reading data with Python Pandas
  • Exploratory data analysis, data visualization, and application of key statistical functions
  • Integration of Six Sigma methods in Python
  • Data preparation and feature engineering - the right data structure for every algorithm
  • Supervised learning - application areas, algorithms and model training
  • Unsupervised learning - application areas, algorithms and model training
  • Model evaluation and performance metrics
  • Model inference and special features when used in batch or real-time applications
  • Automated Model Training and Management - Best Practices


We offer this training in cooperation with our partner Deutsche Gesellschaft für Qualität (DGQ).

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