Gain insights into data visualization with ggplot2, explore data wrangling, and learn about regression modeling using R and Tidyverse. Discover statistical inference through computational methods with the infer package.

Rp.tar.gz

datascience

Rlang

The world is full of data and it can be quite challenging to infer the knowledge based on statistical reasoning. This course aims to equip you with the skills you need to play with your data, wrangle it, and visualize it.  

Starting with data visualization, the course gets learners building ggplot2 graphs early on and then continues to reinforce important concepts graphically throughout the course. After moving through data wrangling and data importing, you’ll be introduced to modeling, which plays a prominent role, with a focus on building regression models and inference for regression. Lastly, statistical inference is presented through a computational lens with calculations done via the infer package.

By the end of this course, you’ll develop your data science toolbox, equipping yourself with tools such as data visualization, data formatting, data wrangling, and data modeling using regression.

Data-Centric Statistical Inference Using R and Tidyverse

# Fit regression model:
debt_model <- lm(debt ~ credit_limit + income, data = credit_ch6)
# Get regression table:
get_regression_table(debt_model)

Learn about the regression plane in multiple regression.

Getting Started with Data in R

Data Visualization

Data Wrangling

Data Importing and “Tidy” Data

Basic Regression

Multiple Regression

Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

Hypothesis Testing

Inference for Regression

Tell a Story with Data

Appendix

Regression Plane