1.2 Who This Book Is For

This course is aligned with enterprise data scenarios within a Cross-Industry Process for Data Mining (CRISP-DM) framework. However, if you had prior expereince with SEMMA, DSLC, OSEMN or even the scientific method; the CRISP-DM framework will provide a similar structured approach to data analytics projects.

You will be re-applying some of your prerequisite skills in database application using MS SQL Server, data visualization using Excel and Tableau, descriptive and inferential statistics for diagnostics with Excel, data mining, and predictive analytics modeling with an analytic platform (such as both KNIME, Azure ML, or AWS).

Lastly, this course content will expand your Python programming mastery beyond MLR models to predictive analytic models you may ahve experienced and then expand into prescriptive analytics applications. Excel is well-suited for many prescriptive analytic models and, where relevant, will be contrasted with Python in many topics. We will expand on why Excel is still a good tool for prescriptive analytics.