Courses

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.

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

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

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

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

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

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.

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.

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

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

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

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.

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

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

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

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.

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

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

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 basic data visualization principles and how to apply them using ggplot2.

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

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

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

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

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

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

Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.