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Last Update	09/20/2026
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Section 1: Getting Started

4Lessons ·
- 1: Installing R and RStudio
  
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- 3: R Package Ecosystem Overview
  
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Section 2: Core Syntax

5Lessons ·
- 7: Writing Functions in R
  
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- 8: Vectorized Operations
  
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Section 3: Data Structures

5Lessons ·
- 13: Working with NA and Missing Data
  
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- 14: Tibbles vs Base Data Frames
  
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Section 4: Data Manipulation

7Lessons ·
- 17: Introduction to dplyr
  
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- 20: Joining Data Frames
  
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- 21: The Pipe Operator in R
  
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Section 5: Visualization and Statistics

6Lessons ·
- 23: Introduction to ggplot2
  
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Section 6: Advanced R

6Lessons ·
- 29: Writing and Publishing R Packages
  
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- 30: Debugging R Code
  
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- 32: Functional Programming Concepts in R
  
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Section 7: Practical Projects

4Lessons ·
- 34: Building a Data Cleaning Pipeline in R
  
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- 35: Building an R Markdown Report
  
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- 36: Building a Simple Shiny Dashboard
  
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Section 8: Interview Practice

2Lessons ·
- 39: Comparing R and Python for Data Analysis
  
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Section 9: More Practice Exercises

3Lessons ·
- 40: Practice Exercise: Cleaning a Messy Real-World Dataset
  
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- 41: Practice Exercise: Building a Custom Summary Statistics Function
  
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Section 10: Shiny Apps in Depth

3Lessons ·
- 43: Reactive Programming in Shiny
  
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Section 12: Tidyverse Deep Dive

5Lessons ·
- 50: readr for Fast Data Import
  
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Section 13: Statistical Modeling Deep Dive

5Lessons ·
- 56: Time Series Analysis with forecast Package
  
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- 57: Survival Analysis with survival Package
  
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Section 14: Machine Learning in R

5Lessons ·
- 59: Building Models with caret
  
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- 61: Cross-Validation Techniques in R
  
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- 62: Random Forests with randomForest Package
  
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- 63: Gradient Boosting with xgboost in R
  
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Section 16: R Package Development Deep Dive

5Lessons ·
- 72: Submitting a Package to CRAN
  
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Section 17: R for Reproducible Research

4Lessons ·
- 73: R Markdown Parameterized Reports
  
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- 76: Quarto as the Successor to R Markdown
  
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Section 18: R and Databases

3Lessons ·
- 77: DBI and Database Connections in R
  
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- 79: Working with Big Data via sparklyr
  
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Section 19: R Performance Optimization

4Lessons ·
- 81: Vectorization vs Loops Performance
  
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- 82: Rcpp for C++ Integration
  
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- 83: Parallel Processing with the parallel Package
  
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Section 21: More Shiny Practice

4Lessons ·
- 89: Authentication in Shiny Apps
  
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Section 22: More Practice Exercises

4Lessons ·
- 91: Practice Exercise: Building an End-to-End Data Analysis Pipeline
  
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- 92: Practice Exercise: Building a Predictive Model with tidymodels
  
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Section 24: More Interview and Review

4Lessons ·
- 99: Common R Interview Questions on the Apply Family
  
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- 100: Whiteboard Practice: Data Cleaning Under Time Pressure
  
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- 101: Code Review Checklist for R Scripts
  
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Section 25: R for Business Analytics

4Lessons ·
- 103: Cohort Analysis with R
  
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- 104: Churn Prediction Modeling in R
  
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Section 26: R Text Mining and NLP

4Lessons ·
- 107: Sentiment Analysis in R
  
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- 108: Topic Modeling with Latent Dirichlet Allocation
  
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- 109: Word Embeddings in R
  
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Section 28: R Deep Learning

3Lessons ·
- 114: Keras and TensorFlow Interfaces in R
  
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- 115: Building a Neural Network in R
  
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- 116: Torch for R Basics
  
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Section 29: Advanced Statistical Techniques

5Lessons ·
- 119: Principal Component Analysis in R
  
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Section 30: R Object Systems Deep Dive

4Lessons ·
- 122: S3 Classes and Methods In Depth
  
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Section 31: R Environments and Metaprogramming

4Lessons ·
- 126: Understanding R Environments Deeply
  
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- 127: Non-Standard Evaluation in R
  
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Section 32: R for Finance

3Lessons ·
- 131: Risk Modeling in R
  
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Section 35: R Data Cleaning Deep Dive

4Lessons ·
- 140: Handling Outliers Systematically
  
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Section 38: R Ecosystem and Career

4Lessons ·
- 151: R vs Python: When to Choose Which
  
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- 153: R User Groups and Community Resources
  
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Section 40: R for Experimentation

3Lessons ·
- 159: Multi-Armed Bandit Simulations in R
  
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- 160: Sequential Testing Methods
  
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Section 42: R for Marketing Analytics

4Lessons ·
- 165: Attribution Modeling in R
  
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- 167: Marketing Mix Modeling in R Revisited
  
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Section 43: R Data Import/Export Deep Dive

4Lessons ·
- 171: Working with JSON Data in R
  
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Section 45: R for Actuarial and Insurance Analytics

4Lessons ·
- 177: Loss Modeling in R
  
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- 179: Insurance Pricing Models in R
  
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- 180: Claims Fraud Detection Modeling
  
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Section 46: R Testing and Quality Assurance Deep Dive

5Lessons ·
- 181: Snapshot Testing for R Functions
  
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- 182: Testing Shiny Applications
  
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Section 47: R Version Control and Collaboration

3Lessons ·
- 188: Collaborative R Development with renv and Git
  
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Section 48: R Advanced Functional Programming

5Lessons ·
- 189: Function Factories in R
  
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- 190: Memoization in R
  
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Section 49: R for Supply Chain and Operations

4Lessons ·
- 194: Inventory Optimization Models in R
  
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- 196: Linear Programming with lpSolve
  
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Section 50: More Practice Exercises Round 3

4Lessons ·
- 199: Practice Exercise: Building a Text Classification Model
  
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- 201: Practice Exercise: Building a Statistical Simulation Study
  
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Section 51: R Dashboards and BI Integration

4Lessons ·
- 205: Embedding R Analyses in Web Applications
  
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Section 52: R Data Governance and Ethics

4Lessons ·
- 206: Anonymization Techniques in R
  
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- 207: Reproducibility and Audit Trails
  
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- 208: Bias Detection in Statistical Models
  
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Section 53: More Modeling Practice

5Lessons ·
- 210: Model Interpretability with DALEX
  
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- 211: Feature Importance Analysis in R
  
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- 213: Monitoring Model Drift in Production
  
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- 214: A/B Testing Model Performance Before Deployment
  
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Section 56: More Interview Practice Round 2

4Lessons ·
- 223: Common R Interview Questions on Data Frame Operations
  
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- 224: Common R Interview Questions on Statistical Concepts
  
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- 225: Mock Data Analysis Interview Walkthrough in R
  
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Section 57: R Networking and APIs

4Lessons ·
- 227: Consuming REST APIs from R with httr2
  
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- 229: Authentication Patterns When Calling APIs from R
  
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- 230: Rate Limiting API Calls from R
  
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Section 59: R Notebook and Documentation Practices

4Lessons ·
- 235: Structuring a Reproducible R Project Layout
  
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- 237: Literate Programming Principles in R
  
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- 238: Version-Controlling Data Analysis Notebooks
  
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Section 61: R for A/B Testing at Scale

4Lessons ·
- 244: Sequential A/B Testing with Alpha Spending
  
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Section 62: R Package Ecosystem Deep Dive

4Lessons ·
- 247: Exploring CRAN Task Views
  
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- 250: Building an Internal Company R Package
  
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