Accelerate Data Science Workflows with Zero Code Changes

About this course

This NVIDIA course teaches you how to accelerate your data science workflows using RAPIDS — without changing a single line of code. If you work with pandas and scikit-learn, RAPIDS lets you run the same code on GPUs instead of CPUs, delivering 10-50x speedups on data processing and machine learning tasks. NVIDIA's decades of GPU expertise distill into a practical, hands-on approach that makes GPU acceleration accessible to everyone.

What you'll learn

• How RAPIDS replaces pandas and scikit-learn with GPU-accelerated equivalents while keeping your code unchanged
• Techniques to profile and benchmark your workflows to identify bottlenecks worth accelerating
• Real-world patterns for loading, cleaning, and exploring large datasets 10-50x faster on GPUs
• How to train machine learning models with scikit-learn-compatible APIs running on GPU hardware
• Best practices for integrating RAPIDS into existing data pipelines and Jupyter notebooks
• When and why GPU acceleration makes sense for your specific use case

Who this is for

You're a data analyst, engineer, or scientist who works with pandas and scikit-learn and wants to speed up slow pipelines without rewriting everything. Whether you're processing gigabytes of data or training models on tight timelines, this course shows you how to unlock your GPU's power with minimal friction.

• Data scientists — slash model training time and experiment faster with GPU-accelerated scikit-learn workflows
• Data engineers — process and transform data pipelines 10-50x faster using GPU-native pandas replacements
• Analytics professionals — accelerate exploratory data analysis and reporting on large datasets without learning new tools
• Machine learning engineers — integrate GPU acceleration into production systems while maintaining code compatibility

Prerequisites

You should be comfortable writing Python code and familiar with pandas DataFrames and scikit-learn basics (training models, splitting data). No GPU experience needed — this course assumes you're new to GPU computing.

Why this matters for Indian learners

India's data science talent pool is growing fast, but so is the volume of data companies need to process — banks, fintech startups, e-commerce platforms, and analytics firms increasingly run workloads that demand faster compute. GPU acceleration is now table stakes at companies like Flipkart, Amazon India, and BYJU'S, where teams handle millions of transactions and terabytes of data daily. Learning RAPIDS positions you as someone who can solve expensive scaling problems, making you more valuable in interviews and on the job.

Frequently asked questions

Is this course really free?

Yes, completely free. No hidden fees, no paid certificate upsell — just register, watch, and learn.

How long will it take to complete?

The course takes about 1 hour to complete. Most learners finish it in a single sitting or spread it across 1-2 evenings alongside other work.

Will I get a certificate?

Yes, you'll earn a certificate of completion from NVIDIA upon finishing the course, which you can add to your LinkedIn profile.