English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 204 lectures (28h 23m) | 20.81 GB
Learn R, data analysis, visualization, inference, and regression through real-world statistical practice.
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.
Who this course is for:
- Students & Early-Career Researchers
- Psychology students learning statistics
- Biology and neuroscience majors using R
- Public health data analysis beginners
- Social science undergraduates in research methods
- Graduate students writing theses with data
- Early-career researchers preparing publications
- Students needing reproducible R workflows
- Professionals Transitioning to Data Roles
- Healthcare professionals learning R statistics
- Education researchers analyzing student data
- Nonprofit staff working with survey data
- Policy analysts learning statistical tools
- Professionals moving into data science careers
- People with stats background new to R
- Learners seeking modern tidyverse methods
- Self-Taught & Lifelong Learners
- Beginners wanting a guided R path
- Self-taught coders needing structured learning
- Lifelong learners exploring data science
- Hobbyists wanting real-world data analysis
- Learners preferring clear step-by-step examples
- People seeking intuition, not black-box methods
- Independent learners practicing hands-on R
- machine learning beginners
Table of Contents
Introduction
1 Course Overview
2 Why Use R
3 Prerequisites and How to Rock This Course
4 The Math is Simple, the Challenge is Choice
5 Installing RStudio and Downloading Course Code
6 Policy on Sharing the Code
Overview of Part 1
7 Overview of Part 1
Basic R Coding
8 Markdown and Packages
9 Using R Like a Calculator
10 Variable Types
11 Variable Types Concepts
12 Assignment in R
13 Vectors
14 Arrays and Matrices
15 Advanced Indexing with Matrices
16 Lists
17 Lists, the Double Square Braket
18 File Paths
19 Data Frames and Tibbles part 1
20 Data Frames and Tibbles part 2
21 If Statements
22 Vectorized If Statements
23 For Loops
24 Logs and Exponents
25 Functions part 1
26 Functions part 2
27 Helper Script Code Organization
28 Getting Help From ChatGPT
The Tidyverse and Data Import
29 What is Tidying Data
30 Import Data From the Internet
31 Renaming Variables part 1
32 Renaming Variables part 2
33 Group and Summarize Data
34 Filtering to Get Rid of NA Rows
35 Filtering for Subsetting
36 Selecting
37 Combining Data From Multiple Sources
38 Using Across With Summarize
39 Pivoting Data Frames
40 Importing Text and CSV Files
41 Example Cardiovascular Health Data part 1
42 Example Cardiovascular Health Data part 2
GGPlot and Creating Great Graphs
43 GGplot and the Grammar of Graphics
44 Lines and Scatter Plots part 1
45 Lines and Scatter Plot part 2
46 Bar Plots
47 Histograms
48 Aesthetic Customization
Part 2 Overview
49 Part 2 Overview
What Are (is) Data
50 Are Data Plural
51 What to Measure
52 Accuracy and Precision
53 Types of Data
54 Samples V. Populations
55 Case Studies and Anecdotes
56 Faking Data
Simulating Data From Different Distributions
57 Project Descriptions and Goals
58 Simulate Random Data From Several Distributions
59 Central Tendency Concepts
60 Central Tendency Calculations part 1
61 Central Tendency Calculations part 2
62 Parametric Variability Concepts
63 Parametric Variability Calculations
64 Non-Parametric Variability Concepts
65 Non-Parametric Variability Calculations
66 Plotting Error Bars to Show Variability
67 Conclusions for Descriptive Stats with Simulated Data
Determining Which Distribution Data Come From
68 Introduction Can Descriptive Stats Tell Us About Distributions
69 Describing Real Data
70 Comparing Empirical Data to Analytic Distributions part 1
71 Comparing Empirical Data to Analytic Distributions part 2
72 Q-Q Plots Concepts
73 Q-Q Plots Calculations part 1
74 Q-Q Plots Calculations part 2
75 What Measures Would You Choose to Describe These Data
Transforming Data
76 How Transforming Data Makes It Interpretable
77 Log Transformation for Normalization Conceptual
78 Log Transformation for Normalization Calculations
79 Constant Value Transformations
80 Properties of the Normal Distribution
81 Z-score Conceptual
82 Z-score Calculations part 1
83 Z-score Calculations part 2
84 Model-Based vs. Empirical Probabilities
85 Log and z-Score Transformations Combined
86 Min-Max Scaling Conceptual
87 Min-Max Scaling Calculations
88 Reviewing Transformations
Identify and Remove Outliers
89 How Can We Decide Which Data Are Valid
90 Garbage In Garbage Out
91 Z-scores for Outlier Detection Concepts
92 Z-scores for Outlier Detection part 1
93 Z-scores for Outlier Detection part 2
94 Modified Z-scores Concepts
95 Modified Z-scores Calculations part 1
96 Modified Z-scores Calculations part 2
97 Super Extreme Values
98 Transform and Z-score Combined
99 Dealing With Outliers
100 Importance of Domain Knowledge
101 Reviewing Outlier Concepts
Probability
102 Probability Basic Concepts
103 Probability Versus Proportion
104 Data Types for Probability
105 Calculating Probability
106 Upper and Lower Tails
107 Doing Math with Probability
108 Probability with Flipping Coins
109 Rarity of Multiple Events
Overview of Part 3
110 Overview of Part 3
Z-test
111 Using the Z-test to make Inferences
112 Probability of a Sample
113 Sampling Distribution of the Mean
114 Null Hypothesis Testing
115 Performing a Z-test
116 Outcomes of a Z-test
117 Sample Size and Error part 1
118 Sample Size and Error part 2
119 Central Limit Theorem
120 Z-test Review
T-tests
121 T-tests a More Flexible Mean Comparison
122 Degrees of Freedom
123 One-Sample t-test
124 Confidence Intervals
125 Paired-Sample T-test
126 Independent Samples T-test part 1
127 Independent Samples T-test part2
128 Comparing Paired v. Independent Sample Tests
129 T-test v. z-test
130 Reporting T-test Results
Multiple Comparisons
131 Why are Multiple Comparisons a Problem
132 Simulating Multiple Tests
133 Multiple Comparison Wrap Up
AB Testing
134 Using T-tests To Make Decisions
135 Marketing Data Description
136 Effect Size
137 Calculating Effect Size
138 Conclusions on the Marketing Data
139 Splitting Continuous Data
Power (Effect Size Impacts Choice of Sample Size)
140 What Sample Size to Choose
141 Power When n=1
142 Power Increases With Sample Size
143 Power When N Varies
144 Further Considerations of Power
145 A Useful Tool for Calculating Power
Wilcoxon Rank Test
146 Wilcoxon Rank Tests Are Non-Parametric
147 Wilcoxon Rank Sum Test Calculations
148 Random Sample Example
149 The W Statistic
150 Signed Rank Test For One Sample Concept
151 Signed Rank Test For One Sample Calculation
152 Signed Rank Test For Paired Samples
153 When To Use the Wilcoxon Tests
Part 4 Overview
154 Part 4 Overview
ANOVAs
155 One-Way ANOVA Concepts
156 One-Way ANOVA Calculations
157 One-Way ANOVA Relation to T-test
158 F to t equivalence calculations
159 Assumptions of the ANOVA
160 Checking the Residuals
161 Post Hoc Testing
162 Using the Tukey Test
163 Two-Way ANOVA Conceptual
164 Math of a Two-Way ANOVA
165 Two-Way ANOVA Calculations part 1
166 Two-Way ANOVA Calculations part 2
167 Types of Sums of Squares
168 Tukey Test with Two-Way ANOVA
169 Drawing Conclusions from ANOVA
Correlation
170 Correlation For Continuous Relationships
171 Pearson Correlation
172 Correlation is not Causation
173 Null Hypothesis Testing for Correlation
174 Confidence Intervals for Correlation
175 Pearson Correlation is Linear Only
176 Handling non-Linearity
177 Spearman Correlation Concept
178 Spearman Correlation Calculations
179 Correlation Matrices
180 Correlation Conclusions
Single Predictor Linear Regression
181 Making Predictions With Regression
182 Single Predictor Regression
183 Interpolation versus Extrapolation
184 R Squared Concept
185 R Squared Calculations
186 Regression Significance Testing Concept
187 Regression Significance Testing Calculations
188 Examining Residuals
189 Regression With vs. Without an Intercept
190 Single Predictor Regression Wrap Up
T-test vs. Regression Comparison Project
191 Where Does Plastic Waste Come From
192 Median-Split Test
193 Linear Regression
194 Which Test Was Better
Chi-Squared Test
195 Goodness of Fit Test
196 Why is it Called Goodness of Fit
197 Introducing Brain Data Example
198 Goodness of Fit Test Calculations
199 Two Variable Chi Squared Test For Independence Concept
200 Two Variable Chi Squared Test for Independence Calculations
201 Interpreting the Test for Independence
202 Chi Squared Conclusions
Congratulations!
203 Course is Over!
204 Bonus content
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