Free online courseIntroduction to Programming

Course Description

Programming becomes far more approachable when it is tied to real decisions and real data. This free online course introduces you to programming through R, a language widely used in Data Science and Business Intelligence. Instead of focusing on abstract syntax alone, you will learn how code helps you represent information, ask better questions, and turn raw data into insights you can trust. It is designed for beginners and career-changers who want a practical starting point, as well as professionals who need a structured refresher for data-driven work.

From the start, you will build confidence with core concepts that make data projects work: how data is represented in a computer, how to transform it into usable shapes, and how to apply functions to automate repetitive tasks. Along the way, you develop a clear mental model of working with data frames, writing reusable logic, and choosing the right approach for common problems such as creating new variables from conditions, sorting and filtering information, and preparing datasets so they are consistent and analysis-ready. These are the same fundamentals used daily in analytics roles, from reporting to more advanced modeling workflows.

As you progress, the course guides you into tidying messy datasets and producing clear visualizations that communicate trends and comparisons effectively. You will practice building plots that reveal change over time and patterns across categories, strengthening your ability to translate numbers into stories that stakeholders can understand. You will also learn good habits for writing reliable programs by testing your code and handling unexpected input, an essential step when moving from personal scripts to tools that others can use safely.

To round out the experience, you explore how to package your work, which is a key professional skill for maintaining code, sharing it with teams, and reusing it across projects. By the end, you will have a solid foundation in programming with R for data analysis, plus the workflow mindset that supports clean, understandable, and dependable results in real BI contexts.