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R Essential Training Part 2: Modeling Data

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Overview

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Learn how to model data in R, one of the most important tools available for data analysis, machine learning, and data science.

Syllabus

Introduction

Model data with R
Using the exercise files

1. R for Data Science

Data science with R: A case study

2. Exploring Data

Computing frequencies
Computing descriptive statistics
Computing correlations
Creating contingency tables
Conducting a principal component analysis
Conducting an item analysis
Conducting a confirmatory factor analysis

3. Analyzing Data

Comparing proportions
Comparing one mean to a population: One-sample t-test
Comparing paired means: Paired samples t-test
Comparing two means: Independent samples t-test
Comparing multiple means: One-factor analysis of variance
Comparing means with multiple categorical predictors: Factorial analysis of variance

4. Predicting Outcomes

Predicting outcomes with linear regression
Predicting outcomes with lasso regression
Predicting outcomes with quantile regression
Predicting outcomes with logistic regression
Predicting outcomes with Poisson or log-linear regression
Assessing predictions with blocked-entry models

5. Clustering and Classifying Cases

Grouping cases with hierarchical clustering
Grouping cases with k-means clustering
Classifying cases with k-nearest neighbors
Classifying cases with decision tree analysis
Creating ensemble models with random forest classification

Conclusion

Next steps

Taught by

Barton Poulson

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4.7 rating at LinkedIn Learning based on 66 ratings

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