Accelerated Data Science with NVIDIA RAPIDS
Speed up pandas, scikit-learn-style ML and Spark workloads on GPUs with the open-source RAPIDS libraries.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Mon, 23 Nov 2026 · 24 hours · 3 days
Accelerated Data Science with NVIDIA RAPIDS
Duration: 24 hours · 3 days
Next batch: Mon, 23 Nov 2026
Format: Online live · Bengaluru classroom
Modules: 7 · Topics: 34
Level: Intermediate
Fee: ₹10,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| Accelerated Data Science with NVIDIA RAPIDS | Mon, 23 Nov 2026 | Online live | Weekday | 3 days | Enquire |
| Accelerated Data Science with NVIDIA RAPIDS | Sat, 09 Jan 2027 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
| Accelerated Data Science with NVIDIA RAPIDS | Mon, 15 Feb 2027 | Bengaluru classroom | Weekday | 3 days | Enquire |
Soaring demand, and where it takes you
Data volumes keep growing, and teams that can shorten data preparation and model training cycles with GPUs gain a practical edge.
Become an expert
Roles this programme prepares you for:
Become a Accelerated Data Scientist
Runs data prep and ML on GPUs to iterate faster.
- Explain when GPUs speed up data workloads
- Accelerate existing pandas code with cudf.pandas
- Train models with cuML using a familiar API
About the course
Learn to accelerate data science and data engineering workloads on NVIDIA GPUs with the open-source RAPIDS libraries: cuDF and the cudf.pandas accelerator, cuML, cuGraph, multi-GPU scaling with Dask and the RAPIDS Accelerator for Apache Spark. Much of it requires few code changes.
Speed up pandas, scikit-learn-style ML and Spark workloads on GPUs with the open-source RAPIDS libraries.
Why should I attend?
Data scientists using pandas and scikit-learn
Data engineers running Spark
ML engineers
Analytics platform teams
Prerequisites
Python, pandas and scikit-learn experience; GPU environment provided
Level
Intermediate
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 24 hours · 3 days
Course fee (open batch): ₹10,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Explain when GPUs speed up data workloads
- Accelerate existing pandas code with cudf.pandas
- Train models with cuML using a familiar API
- Run graph analytics with cuGraph
- Scale to multiple GPUs with Dask
- Use the RAPIDS Accelerator for Apache Spark
Skills you will gain
DataFrames
Machine learning
Scale
Tools and platforms covered
Dask
Apache Spark
Python
JupyterCourse curriculum
M01 GPU data science basics
- GPU vs CPU for data
- The RAPIDS ecosystem
- Setting up environments
- Memory management
- Docker and conda setup
🧪 Hands-on: Run a first cuDF workflow
M02 cuDF and cudf.pandas
- The cuDF API
- Zero-code-change acceleration
- Fallbacks and limits
- String and datetime operations
- Profiling cudf.pandas
🧪 Hands-on: Accelerate a pandas notebook
M03 Machine learning with cuML
- Familiar estimator API
- Common algorithms
- Comparing with scikit-learn
- Hyperparameter search on GPU
- XGBoost on GPU
🧪 Hands-on: Train and benchmark models
M04 Graph analytics
- cuGraph basics
- PageRank and community detection
- Use cases
- Graph construction from tables
- Visualising graph results
🧪 Hands-on: Analyse a network dataset
M05 Scaling out
- Dask with GPUs
- Partitioning
- Multi-GPU workflows
- Dask dashboards
- Out-of-memory strategies
🧪 Hands-on: Scale a pipeline across GPUs
M06 Spark acceleration
- RAPIDS Accelerator for Apache Spark
- Configuration
- Measuring benefit
- Spark qualification tool
- Cost comparison
🧪 Hands-on: Accelerate a Spark job
M07 Capstone: GPU data pipeline
- Baseline CPU pipeline
- GPU port
- Benchmark
- Recommendation
🧪 Hands-on: Port a pandas + scikit-learn pipeline to RAPIDS and report the speed-up
Certification preparation
This course covers topics that can help participants prepare for the NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) and NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS) exam, which is run by NVIDIA. Check the current exam guide on nvidia.com. Passing is not guaranteed and the exam fee is not included unless stated. [confirm]
Hands-on projects
Pipeline acceleration study
Port an existing pandas and ML pipeline to RAPIDS and report the benchmark results.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for Accelerated Data Science with NVIDIA RAPIDS. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Do we need to rewrite our code?
Often not much: cudf.pandas can accelerate many pandas workloads with minimal changes.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹10,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
24 hours · 3 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
Learn to accelerate data science and data engineering workloads on NVIDIA GPUs with the open-source RAPIDS libraries: cuDF and the cudf.pandas accelerator, cuML, cuGraph, multi-GPU scaling with Dask and the RAPIDS Accelerator for Apache Spark. Much of it requires few code changes.
What is Accelerated Data Science with NVIDIA RAPIDS?
Speed up pandas, scikit-learn-style ML and Spark workloads on GPUs with the open-source RAPIDS libraries.
Who this course is for
Data scientists using pandas and scikit-learn, Data engineers running Spark, ML engineers, Analytics platform teams.
What you will be able to do
Explain when GPUs speed up data workloads. Accelerate existing pandas code with cudf.pandas. Train models with cuML using a familiar API. Run graph analytics with cuGraph. Scale to multiple GPUs with Dask. Use the RAPIDS Accelerator for Apache Spark.
Career paths
Roles this course supports include Accelerated Data Scientist, GPU Data Engineer, ML Engineer. Data volumes keep growing, and teams that can shorten data preparation and model training cycles with GPUs gain a practical edge.
NVIDIA & GPU Computing guides
What Is CUDA?
CUDA is NVIDIA's parallel computing platform and programming model for running general-purpose code on NVIDIA GPUs.
What Is NVIDIA RAPIDS?
RAPIDS is a suite of open-source libraries from NVIDIA for GPU-accelerated data science and analytics.
What Are NVIDIA NIM and NeMo?
NIM and NeMo are parts of NVIDIA's software stack for generative AI: NIM for serving models, NeMo for building and customising them.
TensorRT, TensorRT-LLM and Triton Explained
Three NVIDIA technologies for fast, scalable AI inference.