Knowledge Discovery in Databases (KDD) Training in Munich
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. Munich onsite live Knowledge Discovery in Databases (KDD) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Munich
NobleProg München , Landshuter Allee 8-10, München, germany, 80637
Overview
Our training facilities are located at Landshuter Allee 8-10 in Munich. Thanks t...
Overview
Our training facilities are located at Landshuter Allee 8-10 in Munich. Thanks to good transport connections, our spacious training rooms are easy to reach and offer optimal training conditions.
Directions
The NobleProg training facilities are conveniently located not far from the A95 and A96 motorways. The underground station Rotkreuzplatz and the S-Bahn station Donnersbergerbrücke are within walking distance.
Parking spaces
There are numerous free parking spaces around our training rooms.
Local infrastructure
Not far from Landshuter Allee you will find various restaurants and in the nearby Arnulfstrasse there are several hotels.
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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