Courses

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 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.

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

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

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.

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.

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

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.

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

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

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

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

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.

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

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

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

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.

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.

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.

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

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

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

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