Courses

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.

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

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

Stress Management and Resiliency Training (SMART) teaches self-care practices that help participants buffer daily stress, regain a sense of emotional control and enhance quality of life.

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.

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

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.

This course will help leaders of health care organizations develop the skills needed to realize the value of AI in health care and advance their careers through demonstrating leadership in digital transformation of health care.

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

The successful implementation of AI solutions in clinical practice requires teams that are skilled in multiple disciplines, including data science, user-centered design, subject-matter expertise, change management, and more. However, the health care field

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

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