Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive course takes you from R programming basics to advanced machine learning and deep learning. You’ll gain hands-on skills in data manipulation, visualization, and statistical modeling using R. The journey begins with RStudio setup and foundational programming, then moves into real-world data projects, including web scraping, data cleaning, and regression models. You'll explore visualization tools like ggplot2 and Plotly, and delve into supervised and unsupervised learning. Advanced modules cover neural networks, CNNs, autoencoders, and Shiny app deployment. You'll also apply PCA, t-SNE, clustering, and reinforcement learning for complex data tasks. This course suits aspiring data scientists, analysts, and ML enthusiasts. Prior coding experience is helpful but not required. It's designed for beginner to intermediate learners.
What you'll learn
proficient use of R programming for data science
ability to manipulate and visualize data effectively
understanding of machine learning techniques, including supervised and unsupervised learning
experience with real-world data projects such as web scraping and data cleaning
Course objectives
to build foundational skills in R programming
to apply machine learning algorithms to various data tasks
to deploy Shiny applications for data presentation