Knowledge Discovery in Databases (KDD) Training in Dehiwala- Mount Lavinia
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 Dehiwala- Mount Lavinia or in NobleProg corporate training centers in Dehiwala- Mount Lavinia.
NobleProg -- Your Local Training Provider
Dehiwala
No 154, Hotel Road, Dehiwala- Mount Lavinia, sri lanka, 10370
The Centre located in the coastal town of Mount Lavinia, which is just 12 kilometres south of central Colombo and 54 kilometr...
The Centre located in the coastal town of Mount Lavinia, which is just 12 kilometres south of central Colombo and 54 kilometres from the Bandaranaike International Airport and 4-minute walk from the Mount Lavinia railway station, and 4 km from the National Zoological Gardens of Sri Lanka. Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
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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