Knowledge Discovery in Databases (KDD) Training in Leipzig
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 Leipzig or in NobleProg corporate training centers in Leipzig.
NobleProg -- Your Local Training Provider
Leipzig
NobleProg Leipzig, Arthur Hoffmann Str., Leipzig, germany, 04107
Overview
Our training facilities are located at Arthur Hoffmann Str. in Leipzig. Our spacious c...
Overview
Our training facilities are located at Arthur Hoffmann Str. in Leipzig. Our spacious classrooms are centrally located near the university and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Bayerischer Bahnhof S-Bahn station. In the north you reach the motorway A41, in the south the motorway A38.
Parking
You will find numerous parking spaces around our training rooms.
Local Services
You will find numerous parking spaces around 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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