Knowledge Discovery in Databases (KDD) Training in Erfurt
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 Erfurt can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Otto-Schwade-Strasse 8 in Erfurt. Our spa...
Overview
Our training facilities are located at Otto-Schwade-Strasse 8 in Erfurt. Our spacious training rooms are located to the east of Erfurt's old town and offer optimal training conditions for your needs.
Directions
The NobleProg training rooms are easy to reach via the A71 motorway and the B7 federal road. If you arrive by train, it is not far to Erfurt main station with its good ICE connections.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants and hotels in the old town, which can be reached on foot, and as the capital of Thuringia, Erfurt has a lot to offer.
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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