Course Schedule
R Stats Bootcamp Learning Path
This self-paced bootcamp will take most people about 40-60 hours to complete. The schedule below provides an overview of all course modules and resources. Work through the materials sequentially for the best learning experience.
- Discord coming soon
Module 1: R Foundations
Learn the fundamentals of R programming and the RStudio environment.
1. R and RStudio Setup
Getting started with the R programming environment
- Installing R and RStudio
- Navigating the RStudio interface
- Creating your first R script
- Understanding R packages
2. R Language Basics
Core concepts of the R programming language
- R syntax and data types
- Variables and assignment
- Basic operations and calculations
- Control structures (if/else, loops)
3. Functions and Packages
Working with functions and extending R’s capabilities
- Using built-in functions
- Creating your own functions
- Installing and loading packages
- Function documentation and help
4. Data Objects
Understanding R’s data structures
- Vectors, matrices, and arrays
- Lists and factors
- Working with dates and times
- Type conversion and coercion
5. Data Frames
Working with tabular data in R
- Creating and manipulating data frames
- Accessing data frame elements
- Adding and modifying columns
- Merging data frames
6. Data Manipulation
Techniques for cleaning and transforming data
- Filtering and subsetting data
- Sorting and arranging data
- Summarizing data
- Reshaping data (wide vs. long format)
Module 2: Statistical Analysis
Apply R programming to statistical analysis and data visualization.
7. Question, Explore, Analyze
The data analysis workflow
- Formulating research questions
- Exploratory data analysis
- Data visualization principles
- Communicating findings
8. Sampling Distributions
Understanding probability and sampling
- Random sampling
- Probability distributions
- The Central Limit Theorem
- Confidence intervals
9. Correlation
Measuring relationships between variables
- Correlation coefficients
- Visualizing correlations
- Testing correlation significance
- Correlation vs. causation
10. Simple Linear Regression
Modeling relationships between variables
- Linear regression concepts
- Fitting regression models in R
- Interpreting regression output
- Assessing model fit
11. T-tests
Comparing group means
- One-sample t-tests
- Independent samples t-tests
- Paired samples t-tests
- Effect sizes and power
12. One-way ANOVA
Analyzing variance between groups
- ANOVA concepts and assumptions
- Conducting ANOVA in R
- Post-hoc tests
- Reporting ANOVA results
Module 3: Reproducible Research
Learn essential tools and practices for reproducible data science.
13. Reproducibility Principles
Introduction to reproducible research
- Why reproducibility matters
- Components of reproducible workflows
- Documentation best practices
- File organization strategies
14. R Markdown
Creating dynamic documents
- R Markdown basics
- Combining code, results, and narrative
- Document formatting options
- Generating reports in multiple formats
15. Git and GitHub Basics
Version control for data science
- Understanding version control
- Setting up Git and GitHub
- Basic Git workflow
- Tracking changes to your code
16. Collaborative Workflows
Working effectively with others
- Project organization
- Sharing code and data
- Collaboration best practices
- Maintaining reproducibility in teams