Knowledge Discovery in Databases (KDD) Training in Hannover
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 Hannover or in NobleProg corporate training centers in Hannover.
Our training facilities are located at Hildesheimer Strasse 265-267 in Hanover. Our sp...
Overview
Our training facilities are located at Hildesheimer Strasse 265-267 in Hanover. Our spacious training rooms are located near the Hannover Exhibition Centre and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are not far from the B6, the feeder road to the A7 motorway and the A2 motorway can also be reached quickly. The Peiner Strasse stop for public transport is within walking distance.
Parking
Around our training rooms you will find parking spaces which are not subject to charges.
Local Services
There are several restaurants in the vicinity of our training rooms.
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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