Courses

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

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

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

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.

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

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

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

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

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

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

Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design of games such as Super Mario Bros., Pokémon, Angry Birds, and more.

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

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

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

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

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

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

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

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

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

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.

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.

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.

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

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

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

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

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

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

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