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Section 1: Getting Started

4Lessons ·
- 1: Installing R and RStudio
  
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- 2: Working in the R Console and Scripts
  
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- 3: R Package Ecosystem Overview
  
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- 4: Installing and Loading Packages
  
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Section 2: Core Syntax

5Lessons ·
- 5: Vectors and Data Types
  
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- 6: Control Flow Statements
  
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- 7: Writing Functions in R
  
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- 8: Vectorized Operations
  
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- 9: Working with Environments
  
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Section 3: Data Structures

5Lessons ·
- 10: Data Frames
  
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- 11: Lists and Matrices
  
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- 12: Factors
  
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- 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 ·
- 15: Subsetting and Indexing
  
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- 16: Data Cleaning Basics
  
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- 17: Introduction to dplyr
  
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- 18: Reshaping Data with tidyr
  
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- 19: Working with Strings via stringr
  
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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 ·
- 22: Base R Plotting
  
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- 23: Introduction to ggplot2
  
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- 24: Descriptive Statistics in R
  
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- 25: Basic Hypothesis Testing
  
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- 26: Linear Models in R
  
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- 27: Faceting and Themes in ggplot2
  
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Section 6: Advanced R

6Lessons ·
- 28: Apply Family of Functions
  
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- 29: Writing and Publishing R Packages
  
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- 30: Debugging R Code
  
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- 31: Object-Oriented Programming in R (S3, S4, R6)
  
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- 32: Functional Programming Concepts in R
  
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- 33: Environments and Closures 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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- 37: Building an Automated Statistical Report
  
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Section 8: Interview Practice

2Lessons ·
- 38: Common R Interview Questions for Data Roles
  
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- 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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- 42: Practice Exercise: Visualizing Trends with ggplot2
  
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Section 10: Shiny Apps in Depth

3Lessons ·
- 43: Reactive Programming in Shiny
  
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- 44: Building Multi-Page Shiny Apps
  
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- 45: Deploying a Shiny App
  
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Section 11: More Data Wrangling

3Lessons ·
- 46: Working with purrr for Functional Iteration
  
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- 47: Working with data.table for Performance
  
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- 48: Date and Time Handling with lubridate
  
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Section 12: Tidyverse Deep Dive

5Lessons ·
- 49: tibble Package In Depth
  
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- 50: readr for Fast Data Import
  
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- 51: forcats for Factor Manipulation
  
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- 52: purrr Functional Programming In Depth
  
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- 53: tidyselect Helpers
  
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Section 13: Statistical Modeling Deep Dive

5Lessons ·
- 54: Generalized Linear Models in R
  
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- 55: ANOVA and Post-Hoc Tests
  
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- 56: Time Series Analysis with forecast Package
  
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- 57: Survival Analysis with survival Package
  
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- 58: Mixed-Effects Models with lme4
  
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Section 14: Machine Learning in R

5Lessons ·
- 59: Building Models with caret
  
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- 60: Building Models with tidymodels
  
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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 15: R Visualization Deep Dive

4Lessons ·
- 64: Advanced ggplot2: Custom Themes
  
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- 65: Interactive Visualizations with plotly
  
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- 66: Geospatial Visualization with leaflet
  
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- 67: Visualizing Networks with igraph
  
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Section 16: R Package Development Deep Dive

5Lessons ·
- 68: Package Structure and DESCRIPTION File
  
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- 69: Writing Roxygen2 Documentation
  
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- 70: Unit Testing with testthat
  
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- 71: Continuous Integration for R Packages
  
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- 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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- 74: Bookdown for Long-Form Documents
  
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- 75: renv for Reproducible Environments
  
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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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- 78: dbplyr for Remote Data Sources
  
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- 79: Working with Big Data via sparklyr
  
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Section 19: R Performance Optimization

4Lessons ·
- 80: Profiling R Code with profvis
  
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- 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 20: Bioinformatics and Specialized R

3Lessons ·
- 84: Bioconductor Overview
  
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- 85: Genomic Data Analysis Basics in R
  
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- 86: Financial Time Series Analysis in R
  
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Section 21: More Shiny Practice

4Lessons ·
- 87: Shiny Modules for Reusable Components
  
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- 88: golem Framework for Production Shiny Apps
  
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- 89: Authentication in Shiny Apps
  
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- 90: Deploying Shiny Apps to shinyapps.io or Docker
  
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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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- 93: Practice Exercise: Building an Interactive Dashboard with Real Data
  
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- 94: Practice Exercise: Automating a Weekly Reporting Workflow
  
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Section 23: R Data Structures Deep Dive

3Lessons ·
- 95: Working with Nested Lists
  
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- 96: Attributes and Custom Object Classes
  
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- 97: Working with Dates and POSIXct/POSIXlt
  
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Section 24: More Interview and Review

4Lessons ·
- 98: Common R Interview Questions on Vectorization
  
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- 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 ·
- 102: Customer Segmentation Analysis in R
  
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- 103: Cohort Analysis with R
  
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- 104: Churn Prediction Modeling in R
  
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- 105: Market Basket Analysis with arules
  
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Section 26: R Text Mining and NLP

4Lessons ·
- 106: Text Mining with tidytext
  
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- 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 27: R Spatial Data Analysis

4Lessons ·
- 110: Working with sf Package for Spatial Data
  
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- 111: Coordinate Reference Systems in R
  
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- 112: Spatial Joins and Buffer Analysis
  
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- 113: Choropleth Maps 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 ·
- 117: Bayesian Statistics with brms
  
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- 118: Bootstrap Methods in R
  
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- 119: Principal Component Analysis in R
  
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- 120: Cluster Analysis: K-Means and Hierarchical in R
  
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- 121: Factor 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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- 123: S4 Classes and Generic Functions
  
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- 124: R6 Classes for Reference Semantics
  
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- 125: Comparing S3, S4, and R6
  
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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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- 128: Quasiquotation with rlang
  
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- 129: Building a Simple DSL in R
  
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Section 32: R for Finance

3Lessons ·
- 130: Portfolio Analysis with PerformanceAnalytics
  
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- 131: Risk Modeling in R
  
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- 132: Backtesting Trading Strategies in R
  
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Section 33: R for Clinical and Health Data

3Lessons ·
- 133: Clinical Trial Data Analysis in R
  
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- 134: Survival Curves and Kaplan-Meier Estimates Revisited
  
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- 135: Working with Electronic Health Record Data Formats
  
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Section 34: More Shiny Advanced Practice

4Lessons ·
- 136: Reactive Programming Patterns in Shiny Revisited
  
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- 137: Building Multi-Page Shiny Dashboards with bslib
  
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- 138: Real-Time Data Streaming in Shiny
  
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- 139: Custom JavaScript Integration in Shiny
  
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Section 35: R Data Cleaning Deep Dive

4Lessons ·
- 140: Handling Outliers Systematically
  
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- 141: Data Validation with the validate Package
  
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- 142: Fuzzy String Matching for Data Deduplication
  
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- 143: Imputation Techniques for Missing Data
  
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Section 36: R Reporting Automation

3Lessons ·
- 144: Automating Report Generation with cron and R
  
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- 145: Emailing Reports Programmatically from R
  
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- 146: Building a Reusable Report Template System
  
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Section 37: More Practical Projects Round 2

4Lessons ·
- 147: Building a Customer Churn Dashboard
  
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- 148: Building a Financial Portfolio Tracker
  
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- 149: Building an A/B Testing Analysis Pipeline
  
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- 150: Building a Survey Data Analysis Tool
  
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Section 38: R Ecosystem and Career

4Lessons ·
- 151: R vs Python: When to Choose Which
  
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- 152: Contributing to Open Source R Packages
  
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- 153: R User Groups and Community Resources
  
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- 154: Preparing an R Portfolio for Data Roles
  
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Section 39: More Visualization Practice

3Lessons ·
- 155: Practice Exercise: Building a Custom ggplot2 Theme
  
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- 156: Practice Exercise: Building an Animated Visualization with gganimate
  
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- 157: Practice Exercise: Building a Multi-Panel Dashboard Report
  
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Section 40: R for Experimentation

3Lessons ·
- 158: Power Analysis for Sample Size Determination
  
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- 159: Multi-Armed Bandit Simulations in R
  
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- 160: Sequential Testing Methods
  
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Section 41: R for Genomics and Bioinformatics Deep Dive

4Lessons ·
- 161: RNA-Seq Analysis Workflow in R
  
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- 162: Differential Expression Analysis with DESeq2
  
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- 163: Working with Genomic Ranges (GRanges)
  
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- 164: Phylogenetic Tree Analysis in R
  
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Section 42: R for Marketing Analytics

4Lessons ·
- 165: Attribution Modeling in R
  
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- 166: Customer Lifetime Value Calculation
  
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- 167: Marketing Mix Modeling in R Revisited
  
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- 168: Campaign Response Analysis
  
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Section 43: R Data Import/Export Deep Dive

4Lessons ·
- 169: Reading Excel Files with readxl
  
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- 170: Reading SPSS/SAS/Stata Files with haven
  
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- 171: Working with JSON Data in R
  
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- 172: Reading and Writing Parquet Files in R
  
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Section 44: R String Processing Deep Dive

4Lessons ·
- 173: Regular Expressions in R with stringr
  
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- 174: String Distance and Similarity Metrics
  
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- 175: Text Cleaning Pipelines
  
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- 176: Working with Encoding Issues in R
  
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Section 45: R for Actuarial and Insurance Analytics

4Lessons ·
- 177: Loss Modeling in R
  
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- 178: Reserve Estimation Techniques
  
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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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- 183: Continuous Integration with GitHub Actions for R
  
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- 184: Code Coverage with covr
  
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- 185: Mutation Testing Concepts for R Functions
  
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Section 47: R Version Control and Collaboration

3Lessons ·
- 186: Git Workflows for R Projects
  
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- 187: RStudio Projects and Version Control Integration
  
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- 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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- 191: Currying and Partial Application in R
  
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- 192: Recursive Functions and Tail Recursion in R
  
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- 193: Practice Exercise: Building a Function Composition Pipeline
  
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Section 49: R for Supply Chain and Operations

4Lessons ·
- 194: Inventory Optimization Models in R
  
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- 195: Demand Forecasting for Supply Chains
  
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- 196: Linear Programming with lpSolve
  
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- 197: Simulation Modeling for Logistics Networks
  
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Section 50: More Practice Exercises Round 3

4Lessons ·
- 198: Practice Exercise: Building a Time Series Forecasting Pipeline
  
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- 199: Practice Exercise: Building a Text Classification Model
  
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- 200: Practice Exercise: Building a Geospatial Analysis Report
  
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- 201: Practice Exercise: Building a Statistical Simulation Study
  
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Section 51: R Dashboards and BI Integration

4Lessons ·
- 202: Connecting R to Power BI
  
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- 203: Connecting R to Tableau
  
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- 204: Building Executive Dashboards with flexdashboard
  
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- 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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- 209: Fairness Metrics for Predictive Models in R
  
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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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- 212: Model Deployment as a Plumber API
  
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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 54: R Final Capstone Projects

4Lessons ·
- 215: Capstone: Building a Complete Retail Analytics Platform
  
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- 216: Capstone: Building an End-to-End Predictive Maintenance Model
  
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- 217: Capstone: Building a Public Health Surveillance Dashboard
  
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- 218: Capstone: Building a Customer Segmentation and Targeting System
  
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Section 55: R for Sports Analytics

4Lessons ·
- 219: Player Performance Modeling in R
  
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- 220: Win Probability Models
  
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- 221: Sports Data Visualization Techniques
  
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- 222: Fantasy Sports Projection Models
  
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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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- 226: Timed Mock Assessment on Core R and Tidyverse
  
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Section 57: R Networking and APIs

4Lessons ·
- 227: Consuming REST APIs from R with httr2
  
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- 228: Building a Simple API with plumber
  
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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 58: R for Environmental Science

4Lessons ·
- 231: Climate Data Analysis in R
  
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- 232: Ecological Modeling Basics
  
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- 233: Remote Sensing Data Processing in R
  
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- 234: Air Quality Data Analysis in R
  
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Section 59: R Notebook and Documentation Practices

4Lessons ·
- 235: Structuring a Reproducible R Project Layout
  
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- 236: Writing Effective Analysis Documentation
  
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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 60: More Data Wrangling Mastery

4Lessons ·
- 239: Practice Exercise: Reshaping Wide-to-Long Data with Complex Keys
  
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- 240: Practice Exercise: Merging Multiple Messy Data Sources
  
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- 241: Practice Exercise: Building a Data Quality Report Generator
  
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- 242: Practice Exercise: Automating Data Validation Rules
  
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Section 61: R for A/B Testing at Scale

4Lessons ·
- 243: Multiple Testing Correction Methods
  
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- 244: Sequential A/B Testing with Alpha Spending
  
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- 245: Bayesian A/B Testing Approaches
  
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- 246: Analyzing Novelty and Primacy Effects
  
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Section 62: R Package Ecosystem Deep Dive

4Lessons ·
- 247: Exploring CRAN Task Views
  
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- 248: Evaluating Package Quality and Maintenance
  
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- 249: Contributing Fixes to Existing R Packages
  
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- 250: Building an Internal Company R Package
  
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