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LinkedIn Learning

The Essential Elements of Predictive Analytics and Data Mining

via LinkedIn Learning

Overview

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Get useful, real-world insights into using predictive analysis and data mining to solve problems.

Syllabus

Introduction
  • Data mining and predictive analytics
1. What Is Data Mining and Predictive Analytics?
  • Introducing the essential elements
  • Defining data mining
  • Introducing CRISP-DM
2. 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
3. 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
4. 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
5. Problems You Will Face
  • Anticipating project challenges
  • Addressing missing data
  • Addressing organizational resistance
  • Addressing models that degrade
6. 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
7. 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
8. 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

Taught by

Keith McCormick

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