Knowledge Discovery in Databases (KDD) Training in Vienna
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 Vienna can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
NobleProg Wien
Modecenterstraße 22, Wien, Austria, 1030
Overview
Our training facilities are located at Modecenterstrasse 22 in Vienna. Our spaci...
Overview
Our training facilities are located at Modecenterstrasse 22 in Vienna. Our spacious training rooms offer optimal training conditions for your needs.
For questions and training registrations, please contact our company headquarters:
NobleProg Europe GmbH,
Kolonnenstr. 8
10827 Berlin
Deutschland
Directions
The NobleProg training facilities are conveniently located near the A23 motorway and the nearest subway station, within walking distance, is the Zippererstrasse station.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants within walking distance.
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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