Knowledge Discovery in Databases (KDD) Training in Paris
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. Paris 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
Paris, Opera Bourse
Nobleprog Paris, 15 Rue Taitbout, Paris, france, 75009
At the corner of Boulevard Haussmann and Rue Taitbout, the Multiburo Opera-Bourse business center is located in the heart ...
At the corner of Boulevard Haussmann and Rue Taitbout, the Multiburo Opera-Bourse business center is located in the heart of the largest financial center in Paris, but also within walking distance of the department stores. This area between the Paris Opera and the Bourse is always very attractive due to its cultural aspects and its economic dynamism. The establishment of seats of international groups (in the financial and insurance sector) shows the renown of the places
Access:
Train station
10 min by foot far from Saint-Lazare
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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