A general understanding of databases
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 course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
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.
KDD vs data mining
Establishing the application domain
Establishing relevant prior knowledge
Understanding the goal of the investigation
Creating a target data set
Data cleaning and preprocessing
Data reduction and projection
Choosing the data mining task
Choosing the data mining algorithms
Interpreting the mined patterns