Courses

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

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

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

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.

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

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

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

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

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

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

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

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

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

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

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

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

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

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

This course picks up where CS50 leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.

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.

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

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

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

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

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

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.

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

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

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