​As datasets grow larger and models more complex, traditional CPU workflows struggle to keep up with modern data demands. Accelerated data science bridges that gap by moving core analytics and machine learning tasks onto GPUs, where thousands of operations can run in parallel. In this lesson, we’ll explore how NVIDIA’s RAPIDS ecosystem lets data scientists use familiar Python tools—like pandas and scikit-learn—but with GPU-level performance. The goal is simple: understand how acceleration changes the way we approach data, and how to use these tools to make workflows faster, smarter, and more efficient.

​Lesson Objectives:

1. ​Understand the principles of GPU-accelerated data science

2. ​Apply RAPIDS libraries for data manipulation, machine learning and data graphs

3. ​Learn about end-to-end accelerated workflows within real-world scenarios

​Masterclass Prerequisites:

1. ​Register for NVIDIA's Developer Program: https://developer.nvidia.com/login 

2. ​Enroll to this NVIDIA Deep Learning Institute Course: https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-DS-03+V1  (We will do some hands-on, practical exercises in this class using the course)

​Speaker Profile:

​Name: Arthur Peng

​
Bio: Arthur is an AI instructor based in California. He is deeply passionate about the field, especially when it comes to teaching it to others. Over the past year, Arthur has dedicated himself to understanding core AI/ML concepts. He enjoys creating hands-on tutorials and visual aids to help peers and students grasp complex ideas more easily.