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

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

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...

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

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

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

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

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

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

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

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

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