Courses

Explore the risks and rewards of data privacy and collection.

Master how to use AI in your everyday work and drive impact at your organization. Access on-demand learning modules, expert-led sessions, and Harvard insights that help you apply AI to real business challenges—join today for access through September 2026.

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Learn about the advances in artificial intelligence that are transforming the use of natural language processing.

Combine literary research with data science to find answers in unexpected ways. Learn basic coding tools to help save time and draw insights from thousands of digital documents at once.

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

HKS Executive Education and Harvard's John A. Paulson School of Engineering and Applied Sciences present a webinar featuring Harvard faculty Martin Wattenberg and Fernanda Viégas.

Generative AI: How to Use It and Why It Matters, a flexible, six-week online program from Harvard Kennedy School Executive Education, is designed to demystify this transformative technology.

Learn basic data visualization principles and how to apply them using ggplot2.

Learn to process and convert raw data into formats needed for analysis.  

An introduction to the intellectual enterprises of computer science and the art of programming.

This Harvard Medical School one-year, application-based certificate program helps leaders effect positive change by learning to turn patient data into actionable, transformable knowledge.

Build a foundational understanding of AI concepts with this course from Harvard Business School (HBS) Online.

Explore the foundational principles of artificial intelligence (AI) and its transformative applications in medicine and biomedical research.

Discover how artificial intelligence is transforming today's businesses and how you can leverage it to grow your company into an AI-first leader.

Data Science and AI Principles is a Harvard Online course that gives you an overview of data science and AI systems with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, ethics.

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Learn how to effectively use data to tackle your business decisions. Designed for managers, this course provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information.

Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.

Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.

Learn about advances in interpreting biomedical signals and the use of machine learning and deep learning to aid in analysis and diagnosis.

Learn about advances in interpretation of medical images using deep learning systems that can aid in clinical screening and diagnosis.

This course from Harvard Business School (HBS) Online will equip you with a strategic understanding of how AI is transforming organizations.

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

This on-campus executive program is designed to equip leaders with the technical, strategic, and policy foundations necessary to navigate these critical emerging technologies.

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Develop skills in digital research and visualization techniques across subjects and fields within the humanities.

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

Learn inference and modeling: two of the most widely used statistical tools in data analysis.