Knowledge Discovery in Databases (KDD) Training in Stockholm
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 Stockholm or in NobleProg corporate training centers in Stockholm.
NobleProg -- Your Local Training Provider
Stockholm, Hötorget
NobleProg classroom in Scandic Klara Hotel Stockholm, Slöjdgatan 7, Stockholm, sweden, 111 57
Near Hötorget and the nearest station is Hötorget. It is right in the heart of shopping street. With 200 meters (5 minutes ...
Near Hötorget and the nearest station is Hötorget. It is right in the heart of shopping street. With 200 meters (5 minutes walk) to the T-Centralen metro station, it is super easy to get around Stockholm, and 800 meters to the Arlanda Express makes it convenient to get to the airport. The location offers a welcoming and professional atmosphere. It is also conveniently close (15 minutes walk) to the Royal Palace for those who want to take in the major sights.
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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