Knowledge Discovery in Databases (KDD) Training in Coimbra
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. Coimbra onsite live Knowledge Discovery in Databases (KDD) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Hotel Tivoli Coimbra
4, R. João Machado 4, 3000-226 Coimbra, Coimbra, Portugal
Ideally located for a city break, our 4 star hotel in Coimbra also benefits from a region rich in attractions: the historic c...
Ideally located for a city break, our 4 star hotel in Coimbra also benefits from a region rich in attractions: the historic centre, the University Library, the Santa Clara-a-Velha Monastery and the Sé. Nearby is Conímbriga, a well conserved city of the Roman age. For the more active, canoeing on the Mondego River is a must. Families can visit the Portugal dos Pequenitos to see the most emblematic cities and monuments of the country in miniature form.
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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