Whether you’re exploring a career in computer science, looking to strengthen your analytical skills, or simply wanting to better understand and work with data in your field, Duke University offers beginner-friendly programming courses designed for a wide range of professional backgrounds. Knowing the basics of programming and data management and analysis is now even more important in the age of AI. Having this foundational knowledge will allow you to better ensure you have the ability to create high-quality work and best leverage AI tools.
No matter if you work in business, healthcare, research, education, marketing, finance, operations, or another data-informed field, these courses can help you:
Build in-demand coding and analytical skills on your schedule
Learn the foundations of Python or R with no prior experience required
Apply programming to real-world challenges and decision-making
Enroll in one of these beginner-friendly programming courses or specializations today:
Python Programming: Start Your Programming Journey - This practical Python programming course teaches you how to design, plan, implement, and troubleshoot through interactive and individualized coding projects. Whether you're building technical confidence or exploring programming for the first time, this course provides a strong foundation in one of today’s most widely used programming languages.
Programming for Python Data Science - Accelerate your data science journey with hands-on Python training. You’ll learn to manage, analyze, and visualize data using Python libraries like NumPy, Pandas, and Matplotlib. By the end of the series, you'll be prepared to clean datasets, create visualizations, identify patterns, and support data-informed decisions in professional or research settings.
Data Science with R Specialization - This series teaches you to transform, visualize, and ethically analyze data using R—no prior programming experience required. After completing all four courses, you'll be able to clean and analyze data, create compelling visualizations, and apply ethical principles related to data privacy, bias, and misrepresentation—skills increasingly valuable across industries and organizations.
