Harvard University opens applications for seven free online data science courses

Harvard University has opened applications for seven free online courses in data science, offering learners an opportunity to build skills across statistics, modeling, and digital research methods through a flexible study format.

The courses are delivered online and run for eight to nine weeks, requiring about 1 to 2 hours of study per week, with the exception of the capstone course. Interested candidates can apply through the official Harvard platform by June 17, 2026.

The available courses include Visualization, Inference and Modeling, Causal Diagrams: Define Your Hypotheses Before Drawing Conclusions, Capstone, Digital Humanities in Practice: From Research Questions to Results, Probability, and Linear Regression.

• Data Science (Inference and Modeling): The course focuses on using statistical inference and modeling techniques to design methods relevant to real-world applications such as opinion polling.
• Causal Diagrams: Define Your Hypotheses Before Drawing Conclusions begins with five lessons on the fundamentals of causal diagrams and their role in causal inference. It then uses case studies to show how these tools are applied in health and social science research.
• Data Science (Capstone): This is a two-week advanced course that requires a heavier time commitment of 15 to 20 hours per week. The project-based course allows learners to apply R programming and data analysis skills acquired during the broader course series.
• Digital Humanities in Practice: From Research Questions to Results introduces students to the development of search engine components aligned with academic research needs. The course also covers basic text analysis techniques that underpin digital humanities work.
• The Data Science (Probability): The course introduces core statistical concepts, including random variables, independence, Monte Carlo simulations, expected value, standard error, and the central limit theorem.
• Data Science (Linear Regression): This teaches learners how to implement linear regression using R and address confounding factors commonly encountered in real-world data.

Together, the courses are designed to provide a structured introduction to data science concepts while allowing learners to study at their own pace without any enrollment fee.