Courses

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

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.

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

The successful implementation of AI solutions in clinical practice requires teams that are skilled in multiple disciplines, including data science, user-centered design, subject-matter expertise, change management, and more. However, the health care field

An on-campus program to help architects, designers, workplace professionals, and people experts design their workplace for 2026 and beyond.

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

A gentle introduction to programming that prepares you for subsequent courses in coding.

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

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

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

An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.

Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.

Dive into the mechanisms of action and exciting applications of new FDA-approved genetic medicines.

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.

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

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.

Learn how to protect your own data, devices, and systems from today's threats.

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.

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 how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

Discover library design best practices, tour a world-famous library, and apply your new learnings in group design exercises.

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.

This course is a variant of Harvard University's introduction to computer science, CS50, designed especially for lawyers (and law students).

Learn how to create, read, update, and delete data with relational databases.

Apply tools of single-variable calculus to create and analyze mathematical models used by real practitioners in social, life, and physical sciences.

Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.

Show what you’ve learned from the Professional Certificate Program in Data Science.

Learn the significance of electrochemistry, understanding how electrical, chemical, and mechanical energy are linked.

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

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