Knowledge Discovery in Databases (KDD) Training in Montpellier
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 Montpellier can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Montpellier, Garosud
NobleProg Montpellier, 48, rue Claude Balbastre, Montpellier, france, 34070
– Access Tram stop Victoire 2 Line 2 direction Saint Jean de Védas.
-Tram access then Bus take tram line 2 Sabine stop to...
– Access Tram stop Victoire 2 Line 2 direction Saint Jean de Védas.
-Tram access then Bus take tram line 2 Sabine stop towards Saint Jean de Védas then the “La Ronde” bus stop Patrice Lumumba.
- Access by car from the A9 exit 31 “Saint Jean de Védas” Montpellier Ouest, silver cross MOSSON.
-Access by train from Saint Roch station, Tram line 2 towards Saint Jean de Védas, Victory 2 stop.
– Access by plane from Montpellier airport 10 minutes by car, exit 31 of the A9 towards Barcelona or by bus from the airport line 120 to Place de l'Europe then Tram line 2 towards Saint Jean de Védas stop Victory 2
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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