Predictive Analytics Essential Training: Data Mining

Course details

Are you a data science practitioner, looking to develop or enhance your skills in predictive analysis and data mining? This course provides several “big picture” insights, via instructor Keith McCormick, a veteran practitioner who has completed dozens of real-world projects. Keith begins by introducing you to key definitions and processes that you will need to complete the course successfully. He steps you through defining the problem you need your predictive analysis to address, then focuses on how to make sure you meet the data requirements and how good data preparation improves your data mining projects. Keith dives into the skill sets and resources that you need and the problems you will face. Then he goes over the steps to find the solution and put it to work with probabilities, propensities, missing data, meta modeling, and much more. Keith finishes up with detailed explanations of CRISP-DM and Tom Khabaza’s nine laws of data mining, plus Tom’s new 10th law.

Concepts

0. Introduction

01 - Data mining and predictive analytics
02 - Data mining s relevance in the age of AI

1. What Is Data Mining and Predictive Analytics

03 - Introducing the essential elements
04 - Defining data mining
05 - Introducing CRISP-DM

2. Problem Definition

06 - Beginning with a solid first step - Problem definition
07 - Framing the problem in terms of a micro-decision
08 - Why every model needs an effective intervention strategy
09 - Evaluate a project's potential with business metrics and ROI
10 - Translating business problems into data mining problems

3. Data Requirements

11 - Understanding data requirements
12 - Gathering historical data
13 - Meeting the flat file requirement
14 - Determining your target variable
15 - Selecting relevant data
16 - Hints on effective data integration
17 - Understanding feature engineering
18 - Developing your craft

4. Resources You Will Need

19 - Skill sets and resources that you'll need
20 - Compare machine learning and statistics
21 - Assessing team requirements
22 - Budgeting sufficient time
23 - Working with subject matter experts

5. Problems You Will Face

24 - Anticipating project challenges
25 - Addressing missing data
26 - Addressing organizational resistance
27 - Addressing models that degrade

6. Finding the Solution

28 - Preparing for the modeling phase tasks
29 - Searching for optimal solutions
30 - Seeking surprise results
31 - Establishing proof that the model works
32 - Embracing a trial and error approach

7. Putting the Solution to Work

33 - Preparing for the deployment phase
34 - Using probabilities and propensities
35 - Understanding meta modeling
36 - Understanding reproducibility
37 - Preparing for model deployment
38 - How to approach project documentation

8. The Nine Laws of Data Mining

39 - CRISP-DM and the laws of data mining
40 - Understanding CRISP-DM
41 - Advice for using CRISP-DM
42 - Understanding the nine laws of data mining
43 - Understanding the first and second laws
44 - Understanding the data preparation law
45 - Understanding the laws about patterns
46 - Understanding the insight and prediction laws
47 - Understanding the value law
48 - Understanding why models change

Conclusion

49 - Next steps