Program Outline
Fundamentals of Accelerated Data Science
This NVIDIA DLI program teaches teams how to perform multiple analysis tasks on large datasets using RAPIDS, NVIDIA’s collection of accelerated data science libraries. It provides a strong delivery foundation for modern GPU-accelerated data preparation, analysis, and machine learning workflows.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
NVIDIA RAPIDS
Role
Data Scientist
NVIDIA
Accelerated Data ScienceGPU dataframes, ML, graph analytics
NVIDIA RAPIDS
Best Fit
Audience Profile
Who This Program Is For
Built for data scientists who already work with Python-based analytics and want to apply GPU acceleration to real data preparation, analysis, and machine learning workflows.
Overview
Program Summary
Official NVIDIA DLI program focused on end-to-end GPU acceleration for enterprise data science workflows using RAPIDS libraries.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Learn RAPIDS and accelerated workflow fundamentals
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Module 1
Learn RAPIDS and accelerated workflow fundamentals
Build a delivery foundation in NVIDIA RAPIDS for dataframe operations, machine learning, and graph analytics on large datasets.
- RAPIDS foundations
- GPU-accelerated dataframe operations
2Module 2
Apply acceleration across end-to-end data science tasks
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Module 2
Apply acceleration across end-to-end data science tasks
Use GPU-accelerated tools to improve model development, analysis speed, and workflow scalability across real tabular data science scenarios.
- Accelerated machine learning workflows
- Graph and end-to-end data science patterns
Coverage Areas
Topic Coverage
Coverage Item 1
RAPIDS foundations
Coverage Item 2
GPU-accelerated dataframe operations
Coverage Item 3
Accelerated machine learning workflows
Coverage Item 4
Graph and end-to-end data science patterns
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your tabular analytics scenario and representative datasets
- •Add Spark or deployment-focused follow-on modules
- •Extend into enterprise adoption planning for accelerated data science
