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.

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.

Build practical deep learning skills for Python-savvy professionals.

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

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

Learn to use R programming to apply linear models to analyze data in life sciences.

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

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

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

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

Develop a data mindset and the analytical skills to interpret and communicate data while applying concepts to real business problems in this course from Harvard Business School (HBS) Online.

This is CS50’s introduction to computer science and tech literacy for business professionals.

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

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.

A focus on the techniques commonly used to perform statistical inference on high throughput data.

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

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

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.

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

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

Discover how to combine AI-powered analysis with visual storytelling to turn complex data into clear insights that drive smarter decisions.

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

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

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

Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.

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.

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

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