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DataCamp Statistical Modeling in R Part 1

Created/Published/Taught by:
Daniel Kaplan
Nick Carchedi
Tom Jeon

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Free? Partially: Some Free Content, Some Paid

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$0.00 - $29.00

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FromĀ DataCamp:

“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.”

This course consists of five chapters:

  1. What is statistical modeling?
  2. Designing, training, and evaluating models
  3. Assessing prediction performance
  4. Exploring data with models
  5. Covariates and effect size

Part 2 of this course can be found here: Statistical Modeling in R Part 2.

Recommended Prerequisites: Not specified; some familiarity with R

Go to Content: DataCamp – Statistical Modeling in R (Part 1)