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

Aimed at:

Delivery method:

Interactive classroom training combining theory with practical exercises

Prerequisites:

Required preparation:

(Applied) knowledge of at least 1 data science technique

Bring a laptop with RStudio installed

2 hours

Foundation

Duration:

Skill level:

Prefered group size:

+/-10 participants per trainer

Course description

Data Scientists usually boast about one particular algorithm to be used on each and every occasion. But aren't various algorithms more or less suitable to different problems? While many of focus on building trees or (logistic) regression solutions, other popular methods are outlined in this session (SVM and Nearest Neighbour), as well as stacking those solutions. Nevertheless, there is dispute on whether the search for the 'best algorithm fit' for a specific problem is worth the effort. We'll discover this area using a piece of example code in R.

Learning objective

Upon completion of this training, participants will be able to distinguish pros and cons of various algorithmic setups, and be able to use those methods, even in mixed form, for realistic cases.