NVIDIA and the UWI FIC 5IR AI Conference are pleased to invite you to attend an upcoming hands-on technical training workshop:
Workshop Name:
Fundamentals of Accelerated Data Science
Date:
1st and 2nd July 2025
Time:
TBA
Location:
Online
This training is offered exclusively to verifiable conference attendees, academic students, and staff. Instructions will be communicated via email once verified.
About This Workshop
Data science is about using scientific methods, processes, algorithms, and systems to analyze and extract insights from data. It empowers organizations to turn data into a valuable resource, leading to smarter decision-making, improved operations, and enhanced customer experiences. In this workshop, you will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.
Learning Objectives
- Use cuDF to accelerate pandas, Polars, and Dask for analyzing datasets of all sizes efficiently
- Utilize a wide variety of machine learning algorithms, including XGBoost, for different data science problems
- Deploy machine learning models on a Triton Inference Server to deliver optimal performance
- Learn and apply powerful graph algorithms to analyze complex networks with NetworkX and cuGraph
- Perform multiple analysis tasks on massive datasets to stave off a simulated epidemic outbreak effecting the UK
Upon completion, you will be able to perform various data science tasks more efficiently, enabling more iteration cycles and drastically improving productivity.
Workshop Agenda (Delivered across both days)
Introduction (30 mins)
- Meet the instructor.
- Create an account at https://learn.nvidia.com/join
GPU-Accelerated Data Manipulation (2 hours)
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 pandas, Polars, cuDF, and Dask.
- Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.
Break (1 hour)
GPU-Accelerated Machine Learning (2 hours)
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.
Graph Analytics (1.5 hours)
Perform graph analytics:
- Create and analyze graph data on the GPU with cuGraph.
Project: Data Analysis to Save the UK (1 hour)
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.
Prerequisites
- 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
This workshop is brought to you by:
