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

Introduction

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

What Is Data Mining and Predictive Analytics

Introducing the essential elements
Defining data mining
Introducing CRISP-DM

Problem Definition

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

Data Requirements

Understanding data requirements
Gathering historical data
Meeting the flat file requirement
Determining your target variable
Selecting relevant data
Hints on effective data integration
Understanding feature engineering
Developing your craft

Resources You Will Need

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

Problems You Will Face

Anticipating project challenges
Addressing missing data
Addressing organizational resistance
Addressing models that degrade

Finding the Solution

Preparing for the modeling phase tasks
Searching for optimal solutions
Seeking surprise results
Establishing proof that the model works
Embracing a trial and error approach

Putting the Solution to Work

Preparing for the deployment phase
Using probabilities and propensities
Understanding meta modeling
Understanding reproducibility
Preparing for model deployment
How to approach project documentation

The Nine Laws of Data Mining

CRISP-DM and the laws of data mining
Understanding CRISP-DM
Advice for using CRISP-DM
Understanding the nine laws of data mining
Understanding the first and second laws
Understanding the data preparation law
Understanding the laws about patterns
Understanding the insight and prediction laws
Understanding the value law
Understanding why models change

Conclusion

Next steps