Knowledge Discovery in Databases (KDD) Training in Innsbruck
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) training can be carried out locally on customer premises in Innsbruck or in NobleProg corporate training centers in Innsbruck.
NobleProg -- Your Local Training Provider
NobleProg Innsbruck
Valiergasse 58, Innsbruck, Austria, 6020
Overview
Our training facilities are located at Valiergasse 58 in Innsbruck and offer opt...
Overview
Our training facilities are located at Valiergasse 58 in Innsbruck and offer optimal training conditions for your needs.
Directions
The NobleProg training facilities are conveniently located near the main train station, the A12 and A13 motorways are easily accessible.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants in the downtown area and hotels are also 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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