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

Learn what is involved in using data wisely to build a culture of collaborative inquiry. 

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

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

An on-campus program to help architects, designers, workplace professionals, and people experts design their workplace for 2024 and beyond.

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.

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

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

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

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 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.

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

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

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

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

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

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

The structure, annotation, normalization, and interpretation of genome scale assays.

Leading Successful Programs will challenge you to rethink your programs' goals and reassess how you gather, evaluate, and use data to determine their effectiveness.

Learn to use machine learning in Python in this introductory course on artificial intelligence.

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

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.

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.

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

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.

Leading Successful Programs will challenge you to rethink your programs' goals and reassess how you gather, evaluate, and use data to determine their effectiveness.

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