Courses

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

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

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.  

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

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.

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 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.

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

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.

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

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

Build a foundation in R and learn how to wrangle, analyze, and visualize 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 simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.

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

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

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

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