0.4 What to expect in this book

The remainder of the book is organized as follows:

  • Introduce and define key terms and concepts

    • 1. Introduction to Business Analytics

    • 2. AI in Business

  • Database essentials. For audiences with a background in information systems, information technology, or relational databases, these three chapters can be skipped. Check with your instructor to be sure.

    • 3. Relational Databases

    • 4. Data Storage (ERD)

    • 5. Data Retrieval (SQL)

  • Introduce the framework and methodology for data mining projects—the primary topic of the course

    • 6. Data Mining Methodology

  • CRISP-DM Phase 1: Business Understanding

    • 5. Data Mining Methodology (this phase is covered along with the introduction to CRISP-DM in Chapter 6 because it is not the primary focus of this course)

  • CRISP-DM Phase 2: Data Understanding

    • 7. Data Understanding: Visualization

    • 8. Data Understanding: Statistics

  • CRISP-DM Phase 3: Data Preparation

    • 11. ML Studio: Data Cleaning and Preparation (this is covered out of order because I find that students understand this phase better after they've already had exposure to modeling)

  • CRISP-DM Phase 4: Modeling; CRISP-DM Phase 5: Evaluation (these phases are performed iteratively and in unison)

    • 9. Modeling in Excel

    • 10. ML Studio. Introduction to Pipelines

    • 12. ML Studio: Algorithm Selection

    • 13. ML Studio: Feature Selection

    • 14. ML Studio: Optimizing Model Fit and Performance

  • CRISP-DM Phase 6: Deployment

    • 10.5 Deploying the Prediction

  • Up to this point, the book has covered the entire CRISP-DM methodology, but only in the context of traditional regression and classification supervised models. The followoing chapters extend the methodology to more advanced models and techniques.

    • 15. ML Studio: Natural Language Processing

    • 16. ML Studio: Recommendation Engines

  • Final Project to integrate all of the skills learned in this course: a simple deployment of a machine learning model into an Excel-based dashboard

    • 17. Project: Putting it All Together

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