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

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results,...

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

Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train...

Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own...

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

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

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

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

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

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

A focus on the techniques commonly used to perform statistical inference on high throughput data.

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

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

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