Module 3: Statistical Modeling & Machine Learning

Participants will gain in-depth knowledge of modern data modeling, be able to effectively integrate ML techniques into their work, and confidently support data-driven decisions for process improvements.

Key topics of this module:

➡ Multivariate methods and classical regression models (refresher) to refresh statistical basics.
➡ Introduction to data orchestration and machine learning – understanding supervised and unsupervised learning.
➡ Model development using Python, including data preparation, feature engineering, and validation.
➡ Classification and regression methods such as decision trees, Naive Bayes or neural networks.
➡ Differences between classical and ML-based models, including fields of application and limitations.
➡ Practical application of ML methods for process optimization, including visualization and interpretation.
➡ Confident handling of model quality, cross-validation and model comparison for well-founded decisions.