Fundamentals of Accelerated Data Science Training
Course Authors
Fundamentals of Accelerated Data Science Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
With Fundamentals of Accelerated Data Science Training, participants will learn how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS™, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.
This course is part of the following Certifications:
- NVIDIA-Certified Associate: Generative AI Multimodal
- NVIDIA-Certified Associate: Generative AI LLMs
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be canceled, and no refund will be issued, regardless of attendance.
WHAT'S INCLUDED?
- 1 day of Fundamentals of Accelerated Data Science Training with an expert instructor
- Fundamentals of Accelerated Data Science Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Fundamentals of Accelerated Data Science – https://www.wiley.com/
- Fundamentals of Accelerated Data Science Training – https://www.packtpub.com/
- Fundamentals of Accelerated Data Science – https://store.logicaloperations.com/
- Fundamentals of Accelerated Data Science Training – https://us.artechhouse.com/
- Fundamentals of Accelerated Data Science Training – https://www.amazon.com/
RELATED COURSES
ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Fundamentals of Accelerated Data Science Training course, participants can:
- Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
- Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms
- Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time
- Rapidly achieve massive-scale graph analytics using cuGraph routines
CUSTOMIZE IT
- We can adapt this Fundamentals of Accelerated Data Science Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Fundamentals of Accelerated Data Science course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Fundamentals of Accelerated Data Science course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Fundamentals of Accelerated Data Science course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Fundamentals of Accelerated Data Science Training course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Fundamentals of Accelerated Data Science Training course are:
- Experience with Python, ideally including pandas and NumPy.
- Suggested resources to satisfy prerequisites: Kaggle’s pandas Tutorials, Kaggle’s Intro to Machine Learning, Accelerating Data Science Workflows with RAPIDS
COURSE SYLLABUS
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
GPU-Accelerated Data Manipulation
- Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop:
- Read data directly to single and multiple GPUs with cuDF and Dask cuDF.
- Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.
GPU-Accelerated Machine Learning
- Apply several essential machine learning techniques to the data that was prepared in the first section:
- Use supervised and unsupervised GPU-accelerated algorithms with cuML.
- Train XGBoost models with Dask on multiple GPUs.
- Create and analyze graph data on the GPU with cuGraph.
Project: Data Analysis to Save the UK
- Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the entire UK population:
- Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
- Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.
Assessment and Q&A
