Knowledge Discovery in Databases (KDD) Training in Bordeaux
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. Bordeaux onsite live Knowledge Discovery in Databases (KDD) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bordeaux, Mériadeck
Nomade Bordeaux, 39 Rue du Château d'Eau, Bordeaux, france, 33000
Located in the heart of Bordeaux, in the Mériadeck quarter, fully equipped and air-conditioned, NOMADE allows mobile prof...
Located in the heart of Bordeaux, in the Mériadeck quarter, fully equipped and air-conditioned, NOMADE allows mobile professionals to settle down to work and share with other coworkers in a friendly atmosphere.
It offers 23 workstations in open space, a shared meeting room and 5 meeting rooms for rent in Bordeaux center.
The entire space can also be rented in the evening for your events.
Access: TRAM A MERIADECK station, TRAM B GAMBETTA station
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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