“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
NVIDIAIntermediate
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.
Duration
8 hours
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
In Demand
NVIDIA
Accelerated Data ScienceDS
GPU dataframes, ML, graph analytics
NVIDIA RAPIDS
RAPIDS
On this page
Ideal for
Data ScientistData ScienceTailored Team DeliveryImplementation-Focused
Audience Profile
Built for these roles
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
Executive overview
Official NVIDIA DLI program focused on end-to-end GPU acceleration for enterprise data science workflows using RAPIDS libraries.
Readiness
Prerequisites
- Professional data science experience with Python.
- Familiarity with pandas and NumPy.
- Exposure to common machine learning algorithms such as XGBoost, linear regression, DBSCAN, K-Means, or graph analytics.
Program Outcomes
Capabilities your teams will gain
Use RAPIDS libraries for accelerated data science workflows
Work with GPU-accelerated dataframes, ML, and graph analytics
Improve performance across end-to-end tabular analysis tasks
Build stronger readiness for production-scale accelerated analytics
Curriculum
Curriculum roadmap
1
RAPIDS foundations
2
GPU-accelerated dataframe operations
3
Accelerated machine learning workflows
4
Graph and end-to-end data science patterns
1Module 1
Learn RAPIDS and accelerated workflow fundamentals
+
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
+
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
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryIntermediate practitioner depthBusiness outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Helps data teams speed up analytics and machine learning workflows on large datasets by adopting GPU-accelerated data science foundations with RAPIDS.
- •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
Credentials
Certification & official source
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
