Knowledge Discovery in Databases (KDD) Training in Regina
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 Regina or in NobleProg corporate training centers in Regina.
NobleProg -- Your Local Training Provider
Regina - Royal Bank Building
2002 11th Ave, Regina, Canada, S4P 0J3
Grow your business in one of Regina’s most prominent finance hubs. Build your base in the prestigious Royal Bank Building, ...
Grow your business in one of Regina’s most prominent finance hubs. Build your base in the prestigious Royal Bank Building, home to several leading financial and professional services companies.
Impress your clients and colleagues with the building’s commanding city views, before getting to work in stylish surroundings with plenty of natural light. And travel easily for work or pleasure, with the nearby Regina International Airport offering regular flights across Canada and beyond.
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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