R Programming Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to R programming, emphasizing data analysis and statistical computing. Over approximately 57 hours, learners will progress through foundational concepts, programming techniques, data structures, debugging, and simulation. The course is structured into four core modules followed by a final project, combining theoretical knowledge with hands-on coding exercises to build practical proficiency in R. Ideal for those preparing for data science roles, it offers lifetime access and a certificate upon completion.
Module 1: Background, Setup, and Basics
Estimated time: 14 hours
- Introduction to R and its role in statistical computing
- Setting up the R development environment
- Understanding basic R syntax and data types
- Writing and running R scripts
- Using R as a calculator for simple computations
Module 2: Programming with R
Estimated time: 15 hours
- Working with control structures (if-else, for loops, while loops)
- Defining and using user-written functions
- Understanding lexical scoping rules in R
- Writing reusable and modular R code
Module 3: Loop Functions and Debugging
Estimated time: 14 hours
- Applying the apply family of functions (lapply, sapply, apply)
- Using loop alternatives for efficient data processing
- Implementing debugging techniques in R
- Writing robust and error-resistant scripts
Module 4: Simulation and Profiling
Estimated time: 14 hours
- Generating random data for simulations
- Modeling real-world scenarios using R
- Profiling R code for performance optimization
- Improving memory usage and execution speed
Module 5: Final Project
Estimated time: 10 hours
- Apply R programming concepts to a real-world dataset
- Write functions and use control structures effectively
- Profile and debug your code for efficiency
Prerequisites
- Familiarity with basic programming concepts
- Basic understanding of statistics
- Access to a computer with R and RStudio installed
What You'll Be Able to Do After
- Set up and configure the R environment for data analysis
- Work confidently with R data structures such as vectors, lists, and data frames
- Write efficient, reusable R code using functions and control flow
- Debug and profile R programs to improve performance
- Conduct simulations and analyze data using functional programming tools