Fundamentals of Accelerated Data Science

Participants should have:

• Experience with Python programming and common data science libraries
• A foundational understanding of data manipulation and analysis concepts
• Familiarity with basic machine learning principles
• Awareness of pandas or similar dataframe-based workflows

Target audience

This course is designed for:

• Data scientists seeking to accelerate existing Python workflows
• Machine learning engineers working with large or complex datasets
• Developers and analysts exploring GPU-accelerated analytics
• Organisations aiming to scale data science capabilities efficiently

By the end of this workshop, learners will be able to:

• Use cuDF to accelerate pandas, Polars, and Dask workflows for analysing datasets of varying sizes
• Ingest and prepare large and larger-than-memory datasets directly on single or multiple GPUs
• Apply GPU-accelerated supervised and unsupervised machine learning algorithms using cuML
• Use algorithms such as XGBoost to address a range of data science problems
• Create and analyse complex network data using NetworkX and cuGraph
• Deploy machine learning models to an NVIDIA Triton Inference Server for optimised performance
• Integrate multiple large datasets to perform iterative, real-world analysis tasks

Introduction and environment setup

• Meet the instructor and review workshop objectives
• Set up access to the training environment
• Overview of GPU-accelerated data science and the RAPIDS ecosystem
• Understanding the role of GPUs in scalable analytics

GPU-accelerated data manipulation

Ingest and prepare several datasets, including larger-than-memory data, for use in downstream machine learning tasks:

• Reading data directly to single and multiple GPUs using pandas, Polars, cuDF, and Dask
• Comparing CPU-based and GPU-accelerated dataframe operations
• Cleaning and transforming structured datasets on the GPU
• Preparing population, road network, and clinic datasets for analysis
• Managing memory constraints and performance considerations
• Building repeatable, scalable data preparation pipelines

GPU-accelerated machine learning

Apply essential machine learning techniques to prepared datasets using GPU-accelerated libraries:

• Introduction to cuML and GPU-based model training
• Using supervised learning algorithms for predictive modelling
• Applying unsupervised learning techniques for clustering and pattern discovery
• Leveraging XGBoost for classification and regression tasks
• Evaluating model performance and refining hyperparameters
• Understanding performance trade-offs between CPU and GPU workflows

Graph analytics on the GPU

Perform advanced graph analytics to analyse complex networks:

• Introduction to graph data structures and network analysis concepts
• Creating graph data on the GPU using cuGraph
• Analysing connectivity, centrality, and path-based metrics
• Comparing NetworkX and cuGraph implementations
• Scaling graph analytics to large, population-scale datasets

Project: data analysis to support the UK during a simulated epidemic

Apply new GPU-accelerated data manipulation and analysis skills to a population-scale scenario:

• Integrating multiple massive datasets using RAPIDS libraries
• Performing real-world analysis to model and respond to a simulated epidemic affecting the UK population
• Pivoting and iterating on analysis as new simulated daily data becomes available
• Identifying patterns and insights to inform intervention strategies
• Communicating findings clearly and effectively

Inference and deployment considerations

• Preparing trained machine learning models for inference
• Deploying models to an NVIDIA Triton Inference Server
• Validating model performance in a live inference context
• Understanding scalability and operational considerations for production environments

Exams and assessments

Learners complete practical, scenario-based exercises throughout the workshop, culminating in a project that integrates data manipulation, machine learning, and graph analytics techniques.

Assessment is based on applied tasks that evaluate the ability to prepare data, train GPU-accelerated models, perform graph analysis, and interpret results within the simulated epidemic scenario.

Hands-on learning

This workshop is built around applied, GPU-enabled practice:

• Guided exercises using RAPIDS libraries including cuDF, cuML, and cuGraph
• Real-world datasets reflecting population, infrastructure, and healthcare contexts
• Iterative experimentation enabled by accelerated compute performance
• Project-based learning focused on solving a complex, evolving problem