Overview
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.
Requirements
- A general understanding of databases.
Course Outline
Introduction
- 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
Summary and conclusion