Knowledge Discovery in Databases (KDD) Training in Uppsala
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. Uppsala onsite live Knowledge Discovery in Databases (KDD) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
The course location is close to the city center with good access to public transport.
You can discover shopping treasures ...
The course location is close to the city center with good access to public transport.
You can discover shopping treasures at Godsmagasinet, a group of enchanting shops tucked away next to the train station. There is a popular Saturday flea market in Vaksala Square where you can buy souvenirs. You can enjoy a guided candlelit tour of Uppsala Cathedral or admire Viking artefacts, mummies and the eerie 17th-century anatomical theater at the Gustavianum Museum. For a refreshing outdoor afternoon, try a walk along the paths and eat next to the river Fyris, which flows right through the city.
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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