They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling.

From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.

Supported add-ons include: The program provides a platform for experiment selection, recommendation systems, and predictive modelling and is used in biomedicine, bioinformatics, genomic research, and teaching.

In education, it was used for teaching machine learning and data mining methods to students of biology, biomedicine, and informatics.

Over the following years, most contemporary major algorithms for data mining and machine learning were implemented in C++ (Orange's core) or Python modules.