Knowledge Discovery in Databases (KDD) Training in NanJing
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) trainings in NanJing can be carried out locally on customer premises or in NobleProg corporate training centers.
Standing in the heart of city's bustling Xin Jie Kou CBD, Regus business centre is on the 8th floor of Jinling Hotel Asia Pac...
Standing in the heart of city's bustling Xin Jie Kou CBD, Regus business centre is on the 8th floor of Jinling Hotel Asia Pacific Tower which is recognized as a new commercial landmark of Gulou district. This urban complex contains Grand-A offices floors, 5-star hotel, Executive Lounge, restaurants and SPA. It attracts many multinational corporations such as IBM, HP, Citibank, Deloitt, Siemens, etc. for its seamless connection to Metro Line 1 and Line 2 and having local tourist attractions nearby. Nanjing, the capital of Jiangsu Province in Southeast China renowned for its ancient heritage of ten dynasties and spectacular scenery, has now become one of China's most thriving metropolises.
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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