Data Science: R Basics

Chapters

1Getting Started with R and RStudio

2R Language Fundamentals

3Data Types and Structures

4Vectors and Factors

5Matrices, Arrays, and Lists

6Data Frames and Tibbles

7Importing, Exporting, and Reading Data

8Data Cleaning and Transformation with dplyr

9Reshaping Data with tidyr

10Working with Dates, Strings, and Categories

11Exploratory Data Analysis and Visualization with ggplot2

12Control Flow, Functions, and Apply Family

13Reproducibility, Projects, and Packages

14Basics of Statistical Modeling in R

Sampling and random numbers Distributions and density functions Descriptive statistics Hypothesis testing basics t tests and nonparametrics Correlation and covariance Simple linear regression Multiple linear regression Model diagnostics Categorical predictors with factors Logistic regression overview Train test split and CV Metrics RMSE MAE accuracy broom for tidy models Visualizing model results

15Working with Larger Data and Performance

Courses/Data Science: R Basics/Basics of Statistical Modeling in R

Basics of Statistical Modeling in R

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Build and evaluate simple models using base R and tidy modeling tools.

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t tests and nonparametrics

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