Knowledge Discovery in Databases (KDD) Training in Dresden
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 Dresden or in NobleProg corporate training centers in Dresden.
Our training facilities are located at Weißeritzstrasse 30 in Dresden. Our spacious t...
Overview
Our training facilities are located at Weißeritzstrasse 30 in Dresden. Our spacious training rooms are located directly west of Dresden's old town and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the tram stop Kongresszentrum and the A17 motorway is also easily accessible.
Parking
The Mitte multi-storey car park, right around the corner, offers guarded parking spaces.
Local Services
Within walking distance is the Leonardo Hotel Dresden Altstadt, other hotels and a variety of restaurants are also nearby.
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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