Courses

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

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

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

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

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

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

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

Learn what is involved in using data wisely to build a culture of collaborative inquiry. 

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

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

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 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 the significance of electrochemistry, understanding how electrical, chemical, and mechanical energy are linked.

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

Increase your quantitative reasoning skills through a deeper understanding of probability and statistics, with the engaging “Fat Chance” approach that’s made this course a favorite among learners worldwide.

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

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

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

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

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

How can we eradicate malaria? Explore cutting-edge science and technology, and examine policies needed, to control and eliminate malaria.

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

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

Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.

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.

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

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