Aspiring Data Scientists with a wish to deepdive on the most important technologies.

Aimed at:

Delivery method:

Formal training. In the extended version: practice work in R included.

Prerequisites:

Required preparation:

Knowledge of programming basics (preferably, but not necessarily, in R)

In case of the extended trainig, bring a laptop with RStudio installed

3 hours (excl. coding exercises), 4-5 hours (incl. coding exercises)

Practitioner

Duration:

Skill level:

Prefered group size:

8 participants per trainer (scalable)

Course description

This course introduces Classification trees and Ensemble Techniques. Classification trees are popular because they provide intuitive results - which helps both fine-tuning and end-user acceptance. This course explains how they are actually made. Next, ensemble techniques are introduced. these techniques are used in almost all highly optimized data science solutions. How are sub-parts optimally set to work together? There will be the opportunity to try out both methods for ourselves - without the use of your computer! In addition, useful R-examples are provided afterwards, including C5, Random Forests, bagging and stacking.

Learning objective

Upon completion of this training, participants will have a thorough understanding of under-the-hood working of Decision Tree methods and ensemble techniques.