Knowledge Discovery in Databases (KDD) Training in Marseille
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 Marseille or in NobleProg corporate training centers in Marseille.
NobleProg -- Your Local Training Provider
Marseille, Prado Quarter
Mutliburo Marseille, 565 Avenue du Prado, Marseille, france, 13008
At the heart of the prestigious business quarter of Avenue Prado, surrounded by nature and offering a beautiful view on the s...
At the heart of the prestigious business quarter of Avenue Prado, surrounded by nature and offering a beautiful view on the see.
The business center offers companies a wide range of services and technical means with advanced technology, thus encouraging the development of the activity of its customers.
Plane: 40 minutes away fromInternational Airport Marseille-Provence.
Bus: 19 et 83 lines. "La Plage" Station.
Tram: Line 2. Stop " Rond-Point du Prado"
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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