Daniel Kaplan
Daniel Kaplan

DeWitt Wallace Professor at Macalester College

Danny is the DeWitt Wallace Professor of Mathematics, Statistics, and Computer Science at Macalester College in Saint Paul, Minnesota. At Macalester, he has developed the introductory sequence in calculus and statistics as well as an introduction to computing for scientists. He’s co-authored the mosaic R package and written several textbooks: Understanding Nonlinear Dynamics, Introduction to Scientific Computation and Programming, and Statistical Modeling: A Fresh Approach.

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  • Nick Carchedi

    Nick Carchedi

  • Tom Jeon

    Tom Jeon

Course Description

Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In Part 1, we'll take a look at what modeling is and what it's used for, R tools for constructing models, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables. This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses.

  1. 1

    What is statistical modeling?


    This chapter explores what a statistical model is, R objects which build models, and the basic R notation, called formulas used for models.

  2. Designing, training, and evaluating models

    In this chapter, you'll start building models: specifying what variables models should relate to one another and training models on the available data. You'll also provide new inputs to models to generate the corresponding outputs.

  3. Assessing prediction performance

    This chapter is about techniques for deciding whether an explanatory variable improves the prediction performance of a model. You'll use cross validation to compare different models.

  4. Exploring data with models

    This chapter is about constructing models to explore masses of data, for instance to generate hypotheses about what factors are important in how a system works. You'll see how the recursive partitioning model architecture, which has an internal logic for selecting explanatory variables, can be used to explore potentially complex relationships among variables. The chapter also covers the evaluation of prediction performance in models where the response variable is categorical, that is, models used for classification.

  5. Covariates and effect size

    Real-world systems are complicated. To faithfully reflect that complexity, models can incorporate multiple explanatory variables. This chapter introduces the notion of covariates and how they allow you to model the effect of an explanatory variable while taking into account the effects of other variables.