Data scientists and analysts need reliable tools for statistical computing and complex analysis. R programming has become the go-to language for academic research, financial modeling, and machine learning applications, yet many professionals struggle to get started with its syntax and data structures.
This course cuts through the complexity. You’ll learn how to work with R’s core data types, starting with basic operations using the console as a calculator and moving quickly into practical applications. The curriculum focuses on five essential structures: vectors for one-dimensional data, matrices for two-dimensional arrays, factors for categorical information, data frames for complete datasets, and lists for mixed-type collections.
What sets this training apart is its practical approach. You’ll analyze Las Vegas betting data with vectors, work through Star Wars box office numbers using matrices, and manipulate real datasets with data frames. Each concept builds on the previous one, creating a clear learning path from basic arithmetic to complex data manipulation.
Jonathan Cornelissen, co-founder of DataCamp with a PhD in financial econometrics, designed this course to mirror how professionals actually use R. No theoretical overload, just the fundamental skills you need to start your own data analysis projects. The knowledge you gain here feeds directly into advanced tracks for data analysts, data scientists, and R developers.
By the end, you’ll confidently create, subset, name, and compare data structures while understanding when to use each type. These aren’t just academic exercises. They’re the building blocks every R programmer uses daily.
