Master Statistics Using R: Coding, Concepts, Applications

Learn R, data analysis, visualization, inference, and regression through real-world statistical practice.

What you'll learn

R Programming & Data Wrangling

R programming for data analysis

Writing clean reproducible R code

Tidyverse data manipulation skills

Data wrangling with dplyr and tidyr

Visualizing data with ggplot2

Handling messy, real-world datasets

Creating clear, professional plots

Organizing projects for reproducibility

GitHub code-along scripts included

Core Statistical Concepts

Understanding sampling variability

Exploring statistical distributions

Central limit theorem in practice

Standard error and confidence intervals

Logic of hypothesis testing

Null vs alternative hypotheses

P-values and significance testing

Comparing statistical tests effectively

Building analytic intuition hands-on

Inferential Statistics & Modeling

Conducting t-tests in R

ANOVA and group comparisons

Chi-square test for categorical data

Linear regression modeling in R

Understanding assumptions of tests

Interpreting effect sizes in R

Practical Data Analysis

Realistic messy data scenarios

Iterative analysis and refinement

Making decisions with uncertainty

Interpreting results like a researcher

Guided exercises for practice

Step-by-step code demonstrations

Building confidence as a data analyst

Applying statistics to real projects

Requirements

No knowledge or skills are required for this course

Coding experience in any language is helpful but not necessary

Familiarity with basic stats terms like descriptive, inferential, mean, standard deviation, but not necessary

Description

Unlock the power of data by learning statistics the modern way—hands-on, intuitive, and with real-world tools. This course is designed for students, researchers, and professionals who want to move beyond memorizing formulas and truly understand how to analyze data. Using R programming and the tidyverse, you’ll build both the coding fluency and the statistical intuition you need to work like a real analyst.We’ll start at the ground level: organizing messy datasets into tidy data, writing clean and reproducible code, and visualizing information effectively. From there, you’ll gain practical experience with the logic of inference—sampling variability, distributions, confidence intervals, and hypothesis testing—through approachable, step-by-step examples. Along the way, you’ll see how t-tests, chi-square, correlation, and regression all fit together under the same framework.But this isn’t just another lecture-heavy course. You’ll code alongside me with guided exercises, code-along scripts, and real datasets, building a skill set you can apply immediately to assignments, theses, publications, or workplace projects. You’ll also explore more advanced techniques like bootstrapping, resampling, and regression modeling, reinforcing how these tools extend beyond the classroom and into research and professional practice.By the end of this course, you’ll be able to:Write R code that is clean, efficient, and reproducible.Apply a broad set of inferential statistical methods to real data.Visualize results in clear and compelling ways.Develop the confidence to approach data like an experienced analyst.Whether you’re new to statistics, transitioning into a data-focused role, or seeking a stronger foundation for research, this course offers a comprehensive, structured, and practical pathway to mastering statistics with R. Join today, and start building the tools to transform data into knowledge.

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 Why Use R?

Lecture 3 Prerequisites and How to Rock This Course

Lecture 4 The Math is Simple, the Challenge is Choice

Lecture 5 Installing RStudio and Downloading Course Code

Lecture 6 Policy on Sharing the Code

Section 2: Overview of Part 1

Lecture 7 Overview of Part 1

Section 3: Basic R Coding

Lecture 8 Markdown and Packages

Lecture 9 Using R Like a Calculator

Lecture 10 Variable Types

Lecture 11 Variable Types Concepts

Lecture 12 Assignment in R

Lecture 13 Vectors

Lecture 14 Arrays and Matrices

Lecture 15 Advanced Indexing with Matrices

Lecture 16 Lists

Lecture 17 Lists, the Double Square Braket

Lecture 18 File Paths

Lecture 19 Data Frames and Tibbles part 1

Lecture 20 Data Frames and Tibbles part 2

Lecture 21 If Statements

Lecture 22 Vectorized If Statements

Lecture 23 For Loops

Lecture 24 Logs and Exponents

Lecture 25 Functions part 1

Lecture 26 Functions part 2

Lecture 27 Helper Script Code Organization

Lecture 28 Getting Help From ChatGPT

Section 4: The Tidyverse and Data Import

Lecture 29 What is Tidying Data?

Lecture 30 Import Data From the Internet

Lecture 31 Renaming Variables part 1

Lecture 32 Renaming Variables part 2

Lecture 33 Group and Summarize Data

Lecture 34 Filtering to Get Rid of NA Rows

Lecture 35 Filtering for Subsetting

Lecture 36 Selecting

Lecture 37 Combining Data From Multiple Sources

Lecture 38 Using Across With Summarize

Lecture 39 Pivoting Data Frames

Lecture 40 Importing Text and CSV Files

Lecture 41 Example Cardiovascular Health Data part 1

Lecture 42 Example Cardiovascular Health Data part 2

Section 5: GGPlot and Creating Great Graphs

Lecture 43 GGplot and the Grammar of Graphics

Lecture 44 Lines and Scatter Plots part 1

Lecture 45 Lines and Scatter Plot part 2

Lecture 46 Bar Plots

Lecture 47 Histograms

Lecture 48 Aesthetic Customization

Section 6: Part 2 Overview

Lecture 49 Part 2 Overview

Section 7: What Are (is?) Data?

Lecture 50 Are Data Plural?

Lecture 51 What to Measure?

Lecture 52 Accuracy and Precision

Lecture 53 Types of Data

Lecture 54 Samples V. Populations

Lecture 55 Case Studies and Anecdotes

Lecture 56 Faking Data

Section 8: Simulating Data From Different Distributions

Lecture 57 Project Descriptions and Goals

Lecture 58 Simulate Random Data From Several Distributions

Lecture 59 Central Tendency Concepts

Lecture 60 Central Tendency Calculations part 1

Lecture 61 Central Tendency Calculations part 2

Lecture 62 Parametric Variability Concepts

Lecture 63 Parametric Variability Calculations

Lecture 64 Non-Parametric Variability Concepts

Lecture 65 Non-Parametric Variability Calculations

Lecture 66 Plotting Error Bars to Show Variability

Lecture 67 Conclusions for Descriptive Stats with Simulated Data

Section 9: Determining Which Distribution Data Come From

Lecture 68 Introduction: Can Descriptive Stats Tell Us About Distributions?

Lecture 69 Describing Real Data

Lecture 70 Comparing Empirical Data to Analytic Distributions part 1

Lecture 71 Comparing Empirical Data to Analytic Distributions part 2

Lecture 72 Q-Q Plots Concepts

Lecture 73 Q-Q Plots Calculations part 1

Lecture 74 Q-Q Plots Calculations part 2

Lecture 75 What Measures Would You Choose to Describe These Data?

Section 10: Transforming Data

Lecture 76 How Transforming Data Makes It Interpretable

Lecture 77 Log Transformation for Normalization Conceptual

Lecture 78 Log Transformation for Normalization Calculations

Lecture 79 Constant Value Transformations

Lecture 80 Properties of the Normal Distribution

Lecture 81 Z-score Conceptual

Lecture 82 Z-score Calculations part 1

Lecture 83 Z-score Calculations part 2

Lecture 84 Model-Based vs. Empirical Probabilities

Lecture 85 Log and z-Score Transformations Combined

Lecture 86 Min-Max Scaling Conceptual

Lecture 87 Min-Max Scaling Calculations

Lecture 88 Reviewing Transformations

Section 11: Identify and Remove Outliers

Lecture 89 How Can We Decide Which Data Are Valid?

Lecture 90 Garbage In Garbage Out

Lecture 91 Z-scores for Outlier Detection Concepts

Lecture 92 Z-scores for Outlier Detection part 1

Lecture 93 Z-scores for Outlier Detection part 2

Lecture 94 Modified Z-scores Concepts

Lecture 95 Modified Z-scores Calculations part 1

Lecture 96 Modified Z-scores Calculations part 2

Lecture 97 Super Extreme Values

Lecture 98 Transform and Z-score Combined

Lecture 99 Dealing With Outliers

Lecture 100 Importance of Domain Knowledge

Lecture 101 Reviewing Outlier Concepts

Section 12: Probability

Lecture 102 Probability Basic Concepts

Lecture 103 Probability Versus Proportion

Lecture 104 Data Types for Probability

Lecture 105 Calculating Probability

Lecture 106 Upper and Lower Tails

Lecture 107 Doing Math with Probability

Lecture 108 Probability with Flipping Coins

Lecture 109 Rarity of Multiple Events

Section 13: Overview of Part 3

Lecture 110 Overview of Part 3

Section 14: Z-test

Lecture 111 Using the Z-test to make Inferences

Lecture 112 Probability of a Sample

Lecture 113 Sampling Distribution of the Mean

Lecture 114 Null Hypothesis Testing

Lecture 115 Performing a Z-test

Lecture 116 Outcomes of a Z-test

Lecture 117 Sample Size and Error part 1

Lecture 118 Sample Size and Error part 2

Lecture 119 Central Limit Theorem

Lecture 120 Z-test Review

Section 15: T-tests

Lecture 121 T-tests: a More Flexible Mean Comparison

Lecture 122 Degrees of Freedom

Lecture 123 One-Sample t-test

Lecture 124 Confidence Intervals

Lecture 125 Paired-Sample T-test

Lecture 126 Independent Samples T-test part 1

Lecture 127 Independent Samples T-test part2

Lecture 128 Comparing Paired v. Independent Sample Tests

Lecture 129 T-test v. z-test

Lecture 130 Reporting T-test Results

Section 16: Multiple Comparisons

Lecture 131 Why are Multiple Comparisons a Problem?

Lecture 132 Simulating Multiple Tests

Lecture 133 Multiple Comparison Wrap Up

Section 17: A/B Testing

Lecture 134 Using T-tests To Make Decisions

Lecture 135 Marketing Data Description

Lecture 136 Effect Size

Lecture 137 Calculating Effect Size

Lecture 138 Conclusions on the Marketing Data

Lecture 139 Splitting Continuous Data

Section 18: Power (Effect Size Impacts Choice of Sample Size)

Lecture 140 What Sample Size to Choose

Lecture 141 Power When n=1

Lecture 142 Power Increases With Sample Size

Lecture 143 Power When N Varies

Lecture 144 Further Considerations of Power

Lecture 145 A Useful Tool for Calculating Power

Section 19: Wilcoxon Rank Test

Lecture 146 Wilcoxon Rank Tests Are Non-Parametric

Lecture 147 Wilcoxon Rank Sum Test Calculations

Lecture 148 Random Sample Example

Lecture 149 The W Statistic

Lecture 150 Signed Rank Test For One Sample Concept

Lecture 151 Signed Rank Test For One Sample Calculation

Lecture 152 Signed Rank Test For Paired Samples

Lecture 153 When To Use the Wilcoxon Tests

Section 20: Part 4 Overview

Lecture 154 Part 4 Overview

Section 21: ANOVAs

Lecture 155 One-Way ANOVA Concepts

Lecture 156 One-Way ANOVA Calculations

Lecture 157 One-Way ANOVA Relation to T-test

Lecture 158 F to t equivalence calculations

Lecture 159 Assumptions of the ANOVA

Lecture 160 Checking the Residuals

Lecture 161 Post Hoc Testing

Lecture 162 Using the Tukey Test

Lecture 163 Two-Way ANOVA Conceptual

Lecture 164 Math of a Two-Way ANOVA

Lecture 165 Two-Way ANOVA Calculations part 1

Lecture 166 Two-Way ANOVA Calculations part 2

Lecture 167 Types of Sums of Squares

Lecture 168 Tukey Test with Two-Way ANOVA

Lecture 169 Drawing Conclusions from ANOVA

Section 22: Correlation

Lecture 170 Correlation For Continuous Relationships

Lecture 171 Pearson Correlation

Lecture 172 Correlation is not Causation

Lecture 173 Null Hypothesis Testing for Correlation

Lecture 174 Confidence Intervals for Correlation

Lecture 175 Pearson Correlation is Linear Only

Lecture 176 Handling non-Linearity

Lecture 177 Spearman Correlation Concept

Lecture 178 Spearman Correlation Calculations

Lecture 179 Correlation Matrices

Lecture 180 Correlation Conclusions

Section 23: Single Predictor Linear Regression

Lecture 181 Making Predictions With Regression

Lecture 182 Single Predictor Regression

Lecture 183 Interpolation versus Extrapolation

Lecture 184 R Squared Concept

Lecture 185 R Squared Calculations

Lecture 186 Regression Significance Testing Concept

Lecture 187 Regression Significance Testing Calculations

Lecture 188 Examining Residuals

Lecture 189 Regression With vs. Without an Intercept

Lecture 190 Single Predictor Regression Wrap Up

Section 24: T-test vs. Regression Comparison Project

Lecture 191 Where Does Plastic Waste Come From?

Lecture 192 Median-Split Test

Lecture 193 Linear Regression

Lecture 194 Which Test Was Better?

Section 25: Chi-Squared Test

Lecture 195 Goodness of Fit Test

Lecture 196 Why is it Called Goodness of Fit?

Lecture 197 Introducing Brain Data Example

Lecture 198 Goodness of Fit Test Calculations

Lecture 199 Two Variable Chi Squared Test For Independence Concept

Lecture 200 Two Variable Chi Squared Test for Independence Calculations

Lecture 201 Interpreting the Test for Independence

Lecture 202 Chi Squared Conclusions

Section 26: Congratulations!

Lecture 203 Course is Over!

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