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Week 7 (April 21-27)

Now that you are equipped with powerful programming tools we can finally move into the world of applied modeling. You’ll use your new tools of data wrangling and programming to fit, analyze, and understand different applied modelling techniques. First, you will gain a basic understanding of discovering relationships across variables and using models for data exploration. Then you will learn about a few key machine learning algorithms for supervised and unsurpervised learning that every analyst should understand.

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The following tutorials will provide you the knowledge and skills required to start performing applied modeling techniques to your project data set.

1. Model Basics & Model Building: Start learning how models work mechanistically and using them to identify and pull out patterns in your data.
   • Read Chapters 22-25 in R for Data Science.
2. Machine Learning Toolbox: Every analyst should have a basic understanding of a few key analytic techniques. This includes cluster analysis, decision trees, k-nearest neighbors, simple and multivariate regression. Read this introduction to tidymodels to see an overview of how to apply, interpret, and assess these analytic techniques with the Tidyverse.

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Class

Please download this material for Monday’s class: