Knowledge Discovery in Databases (KDD) Training in Chengdu
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 Chengdu or in NobleProg corporate training centers in Chengdu.
The Chengdu Times Plaza Centre is located in one of the most prestigious buildings in the central business district of Chengd...
The Chengdu Times Plaza Centre is located in one of the most prestigious buildings in the central business district of Chengdu. Home to several Fortune 500 companies working in banking, IT and electronics, car manufacturing and construction, the building is also well located for public transport, hotels and retail outlets. Like the building, the surrounding area is a hub for some of the world's largest corporations with Chengdu's international profile continuing to rise. This is due to the government's promotion of the region in China's Go West policy, which is devoted to stimulating economic activity in western China. The city is rapidly becoming the main financial district in western China. Many hi-tech companies have also begun to settle in Chengdu.
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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