Home>Courses>Data-Centric Statistical Inference Using R and Tidyverse

Data-Centric Statistical Inference Using R and Tidyverse

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.

Beginner

99 Lessons

17h

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

311 Playgrounds

10 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

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 promi...Show More

The world is full of data and it can be quite challenging to infer the knowledge based on statistical reasoning. This course aim...Show More

What You'll Learn

Working knowledge of R, ModernDive, and tidyverse to process data

Hands-on experience of data preprocessing, wrangling, and visualization

The ability to apply data-centric concepts for regression analysis, sampling, confidence intervals, and hypothesis testing and interpretation

The ability to tell a story using data of different case studies

The ability to infer insights and conclusions from data using different packages

Working knowledge of R, ModernDive, and tidyverse to process data

Course Content

Getting Started with Data in R

6 Lessons

Get familiar with data analysis in R, from visualization to coding and practical projects.

Getting Started

How Do We Code in R?

Tips on Learning to Code

Explore Our First Dataset

Exploring Data Frames

Test Your Skills on Getting Started with Data in R

Data Visualization

11 Lessons

Get started with visualizing data relationships using ggplot2 in R's Tidyverse framework.

The Grammar of Graphics

When to Avoid Pie Charts

Recap: Data Visualization

Test Your Skills on Data Visualization

Data Wrangling

9 Lessons

Examine data transformation techniques for efficient analysis using dplyr and Tidyverse.

Mutate Existing Variables

Join Data Frames

Other Verbs

Recap: Data Wrangling

Test Your Skills on Data Wrangling

Data Importing and “Tidy” Data

6 Lessons

Grasp the fundamentals of importing and organizing tidy data using Tidyverse in R.

Introduction of Data and Needed Packages

Tidy Data

Converting to Tidy Data

Case Study: Democracy in Guatemala

Tidyverse Package

Test Your Skills on Data Importing and Tidy Data

Basic Regression

13 Lessons

Map out the steps for basic regression principles using R and Tidyverse functionalities.

Introduction and Needed Packages

One Numerical Explanatory Variable

EDA for a Numerical Explanatory Variable

Simple Linear Regression for a Numerical Explanatory Variable

Observed/Fitted Values and Residuals on Numerical Variables

One Categorical Explanatory Variable

Exploratory Data Analysis of One Categorical Explanatory Variable

Linear Regression for One Categorical Explanatory Variable

Observed/Fitted Values and Residuals on Categorical Variables

Correlation Is Not Necessarily Causation

The Best-Fitting Line

The get_regression_x() Functions

Test Your Skills on Basic Regression

Multiple Regression

9 Lessons

Tackle multiple regression, interaction models, residuals, and model selection using visualizations.

Introduction and Exploratory Data Analysis

Interaction Model

Parallel Slopes Model

Observed/Fitted Values and Residuals

Two Numerical Explanatory Variables

Regression Plane

Model Selection Using Visualizations

Correlation Coefficient and Simpson's Paradox

Test Your Skills on Multiple Regression

Statistical Inference with the infer Package

8 Lessons

Master the steps to statistical inference using tactile and virtual sampling techniques, emphasizing randomness and sample sizes.

Sampling

Virtual Sampling

Virtual Sampling Using Different Shovels

Sampling Framework

Statistical Definitions

Case Study: Polls

Recap: Statistical Inference with infer

Test Your Skills on Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

13 Lessons

Sharpen your skills in bootstrapping, resampling, and constructing confidence intervals using the infer package.

Pennies Activity

Resampling

Understanding Confidence Intervals

Constructing Confidence Intervals with the Pennies Sample

Comparing with the Original Workflow

Percentile Method with the infer Package

Interpreting Confidence Intervals

Precise and Shorthand Interpretation

Case Study: Is Yawning Contagious?

Constructing the Confidence Interval with MythBusters Yawn

Interpreting the Confidence Interval

Recap: Bootstrapping and Confidence Intervals

Test Your Skills on Bootstrapping and Confidence Intervals

Hypothesis Testing

9 Lessons

Unpack the core of hypothesis testing, evaluating gender discrimination, movie ratings, and theory-based methods.

Promotions Activity

Understanding Hypothesis Tests

Conducting Hypothesis Tests

Comparison with Confidence Intervals

Interpreting Hypothesis Tests

Case Study: Are Action or Romance Movies Rated Higher?

Theory-Based Hypothesis Tests

Recap: Hypothesis Testing

Test Your Skills on Hypothesis Testing

10.

Inference for Regression

9 Lessons

Master the steps to analyze, interpret, and infer regression relationships using theory and simulations.

Regression Refresher

Interpreting Regression Tables-I

Interpreting Regression Tables-II

Conditions for Inference for Regression-I

Conditions for Inference for Regression-II

Simulation-Based Inference for Regression

Simulation-Based Inference

Recap: Inference for Regression

Test Your Skills on Inference for Regression

11.

Tell a Story with Data

4 Lessons

Grasp the fundamentals of effective data storytelling with R, focusing on analysis and visualization.

Review: Data Science Pipeline

Case Study: Seattle House Prices-I

Case Study: Seattle House Prices-II

Case Study: Effective Data Storytelling

12.

Appendix

2 Lessons

Dig deeper into R, RStudio, and essential R packages for enhanced data analysis.

What are R and RStudio?

What are R Packages?

Course Author

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