- Harvard offers seven free online data science courses lasting eight to nine weeks
- Courses require one to two hours of study weekly, except the capstone which is intensive
- Application deadline for all courses is June 17, 2026, via Harvard's official website
Harvard University is offering seven free data science online courses. The course duration is eight to nine weeks, with one to two hours of study time per week. The last date of application is June 17, 2026 and candidates can visit the official website to apply.
The courses names are 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.
Below you can check about the courses:
1. Data Science: Inference and Modeling
This course explains how to use inference and modeling to develop statistical methods that are useful in conducting effective opinion polls.
2. Causal Diagrams: Define Your Hypotheses Before Drawing Conclusions
The first part of this course consists of five lessons that explain the principles of causal diagrams and their use in the context of causal inference. The second part, through case studies, demonstrates how causal diagrams are applied to real-world situations in health and social sciences.
3. Data Science: Capstone
This is a two-week specialized course, requiring 15 to 20 hours per week. Through this capstone project, students have the opportunity to apply the R data analysis knowledge and skills learned during the course series.
4. Digital Humanities in Practice: From Research Questions to Results
In this course, students work on components of a search engine based on the requirements of academic research. They are also given an understanding of basic text analysis techniques, which are considered the foundation of digital humanities.
5. Data Science: Probability
This course introduces important statistical concepts such as random variables, independence, Monte Carlo simulations, expected value, standard error, and the central limit theorem.
6. Data Science: Linear Regression
This course teaches how to implement linear regression using R and how to balance confounding factors in real-world situations.
