Knowledge Discovery in Databases (KDD) Training in Hangzhou
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. Hangzhou onsite live Knowledge Discovery in Databases (KDD) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
The Hangzhou Foreign Economy & Trade Plaza Centre is in the heart of Hangzhou's central business district. The area i...
The Hangzhou Foreign Economy & Trade Plaza Centre is in the heart of Hangzhou's central business district. The area is home to a vibrant private sector with many multinational companies having set up a base there including those working in banking, IT, electronics, engineering and foreign trade. Just 1.5 hours from Shanghai by train, the city has a growing economy with many more companies attracted to the area. Hangzhou has been one of the wealthiest cities in China for over 1,000 years, mainly due to the stunning natural beauty of the area as well as its location on the Hangzhou Bay. There are many shopping malls, restaurants and hotels within walking distance of the centre.
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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