Share Description
In this course, you will :
- provides that viewpoint through the eyes of a seasoned practitioner who has completed dozens of real-world projects
- He shares his expertise with you. Walk through each stage of a typical project, from defining the problem to gathering data and resources to implementing the solution.
- In addition, it provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine data mining laws, which will keep you focused on strategy and business value.
Syllabus :
1. What Is Data Mining and Predictive Analytics?
- A definition of data mining
- What's data mining and predictive analytics?
- What are the essential elements?
2. Problem Definition
- Determine the business objective
- Identify an intervention strategy
- Estimate the return on investment
- Program management
3. Data Requirements
- Customer footprint
- Flat file
- Understand your target
- Select the data for modeling
- Understand integration
- Understand data construction
4. Resources You'll Need
- Understand data mining algorithms
- Assess team requirements
- Budget time
- Work with subject matter experts
5. Problems You'll Face
- Deal with missing data
- Resolve organizational resistance
- Why models degrade
6. Finding the Solution
- Search the solution space
- Unexpected results
- Trial and error
- Construct proof
7. Putting the Solution to Work
- Understand propensity
- Understand metamodeling
- Understand reproducibility
- Master documentation
- Time to deploy
8. CRISP-DM and the Nine Laws
- 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
