Process Dynamics and Operations

Data Science in Engineering

The course Data Science in Engineering gives an overview of modern methods of Data Science with a focus on process engineering applications. The basics of data pre-treatment such as multicollinearity, linear dependencies, imputation of missing values, anomaly detection, handling of outliers, methods for feature selection and extraction using for example Stepwise Variable Selection, Lasso, L1/L2 regularization and PCA are covered. Based on this, supervised machine learning methods are introduced to solve regression problems. Besides linear methods (Linear Regression, Lasso, Robust Regression), nonlinear methods such as Support Vector Regression, Gaussian Process Regression and Artificial Neural Networks are introduced. Furthermore, for dynamic data-driven modeling, recurrent neural networks are covered.

The methods are explained using examples from chemical engineering or process engineering and the examples are made available to the students. Software frameworks in Python will be used.

Die Prüfung erfolgt in Form einer Semesteraufgabe mit anschließender mündlicher Rücksprache. Inhalt der Semesteraufgabe ist die Anwendung der erlernten Methoden auf einen realen Datensatz aus der Verfahrenstechnik.

LV-Number0339 L XXX 
Module-Number30523 
LectureFridays, 2-4 pm, online; Dr. Alexander Badinski, Joris Weigert 
CycleSummer semester 
Examination formProgramming project (80%), oral consultation (20%) 
Consulting hoursby arrangement with Joris Weigert 

Contact person

Building KWT-A
Room KWT-A 110