Accelerate Data Science Workflows with Zero Code Changes
This self-paced, free course by NVIDIA introduces the RAPIDS suite of libraries, enabling data scientists to accelerate their existing CPU-based workflows without modifying their code. By leveraging GPU acceleration, learners can achieve significant performance improvements in data processing tasks. The course covers how to integrate RAPIDS seamlessly into Python-based data science pipelines, enhancing speed and efficiency.
Who Should Take This Course:
Data scientists and analysts familiar with Python and standard data science libraries like pandas and scikit-learn, who wish to accelerate their workflows using GPU resources.
What You Will Learn:
• Introduction to RAPIDS and its components
• How to accelerate data processing tasks using cuDF and cuML
• Integrating RAPIDS into existing Python workflows
• Best practices for leveraging GPU acceleration in data science
Why This Course Matters:
As data volumes grow, the need for faster processing becomes critical. This course equips professionals with the knowledge to enhance their data science workflows, leading to quicker insights and more efficient analyses.