Knowledge Discovery in Databases (KDD) Training in Stuttgart
Online or onsite, instructor-led live Knowledge Discovery in Databases (KDD) training courses demonstrate through interactive hands-on practice the processes involved in KDD and how to implement those processes.
KDD training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Knowledge Discovery in Databases (KDD) trainings in Stuttgart can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Löffelstraße 40 in Stuttgart. Our spacious tr...
Overview
Our training facilities are located at Löffelstraße 40 in Stuttgart. Our spacious training rooms are easy to reach thanks to good traffic connections and offer optimal training conditions.
Arrival
The NobleProg training facilities are conveniently located near the B27, the feeder road to the A8 motorway. If you arrive by public transport, the nearest underground station is Degerloch/Albstrasse.
Parking
Around our training rooms you will find numerous parking spaces which are not subject to charges.
Local Services
Not far from Löfflerstrasse you will find various restaurants and hotels, including the Hotel Harambe Afrika.
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing.
In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
Audience
Data analysts or anyone interested in learning how to interpret data to solve problems
Format of the Course
After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
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