Learn the basics of data mining and predictive analytics. Explore a real-world project, from defining the problem to putting the solution into practice; review CRISP-DM; and more.
The Essential Elements of Predictive Analytics and Data Mining
Overview
Syllabus
Introduction
- Welcome
- What you should know before watching this course
- Introduction
- A definition of data mining
- What's data mining and predictive analytics?
- What are the essential elements?
- Introduction
- Determine the business objective
- Identify an intervention strategy
- Estimate the return on investment
- Program management
- Introduction
- Customer footprint
- Flat file
- Understand your target
- Select the data for modeling
- Understand integration
- Understand data construction
- Introduction
- Understand data mining algorithms
- Assess team requirements
- Budget time
- Work with subject matter experts
- Introduction
- Deal with missing data
- Resolve organizational resistance
- Why models degrade
- Introduction
- Search the solution space
- Unexpected results
- Trial and error
- Construct proof
- Introduction
- Understand propensity
- Understand metamodeling
- Understand reproducibility
- Master documentation
- Time to deploy
- Introduction
- Understanding CRISP-DM
- Understand laws 1 and 2
- Understand law 3
- Understand laws 4 and 5
- Understand laws 6, 7, and 8
- Understand law 9
- Next steps
Taught by
Keith McCormick
