Courses

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.

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

Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.

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

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

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

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

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

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

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

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

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

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

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

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