Courses

Build practical deep learning skills for Python-savvy professionals.

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

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

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

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

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.

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

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

Build practical skills that will prepare you to interpret data, test ideas, and apply predictive and AI insights to real-world business decisions in this course from Harvard Business School (HBS) Online.

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

This course from Harvard Business School (HBS) Online will teach you the practical skills to turn data insights into strategic action.

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

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

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

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

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.

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

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

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

Sentar las bases de conocimiento en R y aprender a discutir, analizar y visualizar datos.

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

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

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

This 17-week online credential program from Harvard Business School (HBS) Online will teach you the fundamental skills professionals need to succeed today.  

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

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

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