The Essential Elements of Predictive Analytics and Data Mining

Course details

A proper predictive analytics and data-mining project can involve many people and many weeks. There are also many potential errors to avoid. A "big picture" perspective is necessary to keep the project on track. This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules. Here he shares his knowledge with you. Walk through each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Keith also provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine laws of data mining, which will keep you focused on strategy and business value.

Learning objectives
What makes a successful predictive analytics project?
Defining the problem
Selecting the data
Acquiring resources: team, budget, and SMEs
Dealing with missing data
Finding the solution
Putting the solution to work
Overview of CRISP-DM

Concepts

0. Introduction

01 - Welcome
02 - What you should know before watching this course

1. What Is Data Mining and Predictive Analytics

03 - Introduction
04 - A definition of data mining
05 - What's data mining and predictive analytics
06 - What are the essential elements

2. Problem Definition

07 - Introduction
08 - Determine the business objective
09 - Identify an intervention strategy
10 - Estimate the return on investment
11 - Program management

3. Data Requirements

12 - Introduction
13 - Customer footprint
14 - Flat file
15 - Understand your target
16 - Select the data for modeling
17 - Understand integration
18 - Understand data construction

4. Resources You'll Need

19 - Introduction
20 - Understand data mining algorithms
21 - Assess team requirements
22 - Budget time
23 - Work with subject matter experts

5. Problems You'll Face

24 - Introduction
25 - Deal with missing data
26 - Resolve organizational resistance
27 - Why models degrade

6. Finding the Solution

28 - Introduction
29 - Search the solution space
30 - Unexpected results
31 - Trial and error
32 - Construct proof

7. Putting the Solution to Work

33 - Introduction
34 - Understand propensity
35 - Understand metamodeling
36 - Understand reproducibility
37 - Master documentation
38 - Time to deploy

8. CRISP-DM and the Nine Laws

39 - Introduction
40 - Understanding CRISP-DM
41 - Understand laws 1 and 2
42 - Understand law 3
43 - Understand laws 4 and 5
44 - Understand laws 6, 7, and 8
45 - Understand law 9

Conclusion

46 - Next steps