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GPU-Accelerated Data Science Training

Develop the parallel computing, GPU-accelerated data processing, and high-performance data science skills that modern large-scale analytics genuinely demands.

  • UK Licensed
  • Global Standards
  • Expert Instructor
  • 5 Day Training
  • Overview

Course Overview

Most data scientists work at CPU speed. That’s fine until the dataset gets large enough, the model complex enough, or the deadline tight enough that CPU-based processing becomes the bottleneck rather than the analysis itself. The organizations running real-time fraud detection, genomic analysis, and large-scale machine learning at production speed aren’t waiting for CPU pipelines to finish; they’ve moved their workloads to GPU-accelerated computing, and the performance difference is measured in orders of magnitude, not percentage points.

This GPU-Accelerated Learn Data Science Training program covers parallel computing methodology, GPU computing course fundamentals, and high-performance computing applications across data processing, machine learning, and deep learning workloads using Python. It sits within our corporate training programs portfolio alongside incident response training and other professional development programs, bringing the same technically rigorous, hands-on approach to data science with Python and GPU acceleration that we apply across our data science and technology programs.

Whether you’re a data scientist pursuing a recognized data science certificate with GPU specialization or a machine learning engineer optimizing production workloads for speed and scale, this program gives you the parallel computing depth and GPU programming competency that high-performance data science requires.

  • Goals

Course Objectives

By the end of this program, attendees will be able to:

  • Apply parallel computing principles across GPU architecture, thread management, and memory optimization for data science workloads
  • Use Python for data science with GPU acceleration libraries including RAPIDS, CuPy, and PyTorch across realistic large-scale data processing scenarios
  • Implement CUDA data science programming fundamentals for custom GPU kernel development and optimization
  • Accelerate machine learning model training using GPU computing course techniques across scikit-learn, XGBoost, and deep learning frameworks
  • Apply high-performance computing methodology to benchmark, profile, and optimize GPU-accelerated data science pipelines
  • Work toward a recognized data science certificate through a structured, GPU-specialization-focused assessment pathway
  • Key Points

Who Should Attend?

  • Data Scientists Working With Large-Scale Datasets
  • Machine Learning Engineers Optimizing Training Pipelines
  • Deep Learning Practitioners Seeking GPU Performance Skills
  • Data Engineers Building High-Performance Analytics Infrastructure
  • Researchers in Computational Science and Bioinformatics
  • Technical Teams Building GPU-Accelerated Data Science Capability
  • Key Benefits

Key Benefits of the Course

  • A data science certificate program grounded in real GPU-accelerated computing and large-scale data processing scenarios
  • Practical Python for data science GPU acceleration skills applicable to the next model training or data processing assignment
  • Stronger parallel computing capability covering GPU architecture, memory management, and thread optimization
  • Clear CUDA data science and high-performance computing methodology for custom kernel development and pipeline optimization
  • A recognized credential from our Global Industries Intelligence training portfolio supporting progression toward senior data science and ML engineering roles
  • Outline

Course Outline

Day 1: GPU Architecture and Parallel Computing Fundamentals

  • Covers GPU architecture fundamentals: how GPU hardware differs from CPU architecture, and why parallel computing across thousands of GPU cores enables performance gains that serial CPU processing cannot match for data science workloads
  • Introduces the CUDA programming model, including threads, blocks, grids, and the memory hierarchy that determines whether GPU-accelerated code runs efficiently or wastes the hardware advantage through poor memory access patterns
  • Real GPU computing examples show where data scientists who moved workloads to GPU without understanding parallel computing fundamentals produced code that ran slower than CPU implementations and what architectural understanding changes about the result

Day 2: Python for Data Science With GPU Acceleration

  • Covers Python for data science GPU acceleration using RAPIDS cuDF for GPU-accelerated dataframe operations, cuML for GPU-accelerated machine learning, and the workflow integration that replaces CPU pandas and scikit-learn with GPU equivalents without rewriting entire pipelines
  • Introduces CuPy as a GPU-accelerated NumPy replacement for array operations, linear algebra, and the numerical computing workflows that data scientists run constantly and that GPU acceleration makes orders of magnitude faster across large array sizes
  • Attendees complete a realistic data processing pipeline exercise comparing CPU and GPU performance across identical workloads, developing the implementation judgment that determines when GPU acceleration delivers meaningful performance improvement and when overhead costs outweigh the benefit

Day 3: CUDA Data Science Programming

  • Covers CUDA data science programming fundamentals: writing custom GPU kernels in Python using Numba, optimizing memory access patterns, and the profiling discipline that identifies exactly where GPU workloads are spending time versus where they could be running faster
  • Introduces parallel reduction, parallel scan, and the custom kernel patterns that data scientists need when standard library functions don't exist for a specific computational operation
  • Hands-on exercises develop custom GPU kernel implementation skills across realistic data science computation scenarios, building the low-level parallel programming competency that separates GPU data scientists from those who only use pre-built acceleration libraries

Day 4: GPU-Accelerated Machine Learning and Deep Learning

  • Covers GPU-accelerated machine learning training across XGBoost GPU mode, GPU-accelerated ensemble methods, and the training configuration decisions that maximize GPU utilization during model fitting across large datasets
  • Introduces deep learning GPU acceleration using PyTorch model architecture design for GPU efficiency, multi-GPU training strategies, and the mixed precision training techniques that reduce memory requirements while maintaining model accuracy
  • Attendees train and optimize a realistic deep learning model using GPU acceleration, developing the training configuration and performance monitoring skills that production machine learning engineering requires

Day 5: High-Performance Computing Pipeline Design and Optimization

  • Covers high-performance computing pipeline architecture for production data science: how to design end-to-end GPU-accelerated workflows that handle data ingestion, processing, model inference, and result delivery without CPU bottlenecks degrading overall pipeline throughput
  • Introduces GPU profiling and performance optimization methodology: NVIDIA Nsight tools, memory bandwidth analysis, and the systematic optimization approach that finds and fixes the specific bottlenecks limiting GPU pipeline performance
  • Attendees complete a full GPU-accelerated data science pipeline design exercise from data processing through model training and inference optimization with direct instructor feedback on architectural decisions, optimization approach, and performance measurement methodology
  • Terms

Training Methodology

This program combines:

  • GPU-accelerated data science workshops using realistic large-scale datasets and cloud GPU computing environments
  • Parallel computing and CUDA programming exercises with structured peer review and instructor feedback
  • Machine learning and deep learning GPU acceleration sessions built around real production pipeline optimization scenarios
  • Data science certificate preparation aligned to recognized high-performance computing and GPU data science competency standards
  • Achievements

Certification

Attendees receive a course completion certificate from our Corporate & Industrial Training Center. The program develops practical, production-ready data science certificate capability across GPU architecture, parallel computing, Python GPU acceleration, CUDA programming, and high-performance pipeline design. In-house training is available for data science and machine learning engineering teams building GPU acceleration capability across their analytics infrastructure. Contact us now for upcoming dates across our training portfolio.

  • Case Study

Case Study Example

A financial services firm had a fraud detection team running gradient boosting models on CPU infrastructure. Model retraining took four hours overnight, which meant the fraud detection system was always operating on yesterday’s patterns rather than responding to evolving fraud behavior in near real time. After key data science staff completed this GPU-Accelerated Data Science Training program, the model training pipeline was migrated to GPU-accelerated XGBoost using the RAPIDS ecosystem covered in the course. Retraining time dropped from four hours to eleven minutes, enabling intraday model updates that the CPU pipeline had never made operationally feasible. Fraud detection performance improved measurably in the following quarter as the model began responding to emerging fraud patterns within hours rather than days.

  • Why Choose

Why Choose This Program?

Data science training that teaches algorithms without developing the parallel computing and GPU optimization discipline that makes those algorithms run at production scale produces data scientists who understand the methods but can’t deploy them at the speed real applications require. This program is built around the performance engineering mindset that GPU-accelerated data science demands, from architecture fundamentals through production pipeline optimization. Explore our incident response training program and other professional development programs across our training portfolio for a complete view of what Global Industries Intelligence delivers.

  • Connect

Contact Us

Ready to build GPU-accelerated data science capability across your analytics or machine learning team? Contact us now for upcoming dates and in-house training solutions tailored to your organization.

  • FAQs

Fequently Asked Questions

What does the GPU-Accelerated Data Science Training program cover?

Five days across GPU architecture and parallel computing fundamentals, Python GPU acceleration using RAPIDS and CuPy, CUDA data science programming, GPU-accelerated machine learning and deep learning, and high-performance computing pipeline design and optimization with a full pipeline optimization exercise on Day 5.

Python for data science proficiency is required. Prior GPU or CUDA experience is helpful but not essential. The program introduces GPU computing concepts from architectural fundamentals before progressing to implementation, making it accessible for data scientists with strong Python backgrounds who are new to GPU acceleration.

The program uses cloud GPU computing environments; attendees access GPU instances through cloud platforms, so no local GPU hardware is required. This also reflects how most production GPU data science workloads are deployed in practice

Performance gains vary by workload. GPU acceleration delivers the largest benefits on parallelizable computations across large datasets. Typical improvements range from 10x to 100x for data processing and model training workloads, with some specific operations achieving even greater acceleration when properly optimized.

Financial services, healthcare, genomics, autonomous systems, and any organization running large-scale machine learning or deep learning at production speed see the most direct value, though any data science team whose CPU pipeline performance is limiting analytical velocity or model freshness has a use case for building GPU acceleration capability

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Course Schedule

Select your preferred date & venue

20 Jan -- 24 Jan

Ghana

GBP 4595/Person

14 Apr -- 18 Apr

Qatar

GBP 4595/Person

7 Jul -- 11 Jul

Spain

GBP 4595/Person

13 Oct -- 17 Oct

Morocco

GBP 4595/Person

20 Jan -- 24 Jan

Ghana

GBP 3595/Person

14 Apr -- 18 Apr

Qatar

GBP 3595/Person

7 Jul -- 11 Jul

Spain

GBP 3595/Person

13 Oct -- 17 Oct

Morocco

GBP 3595/Person

Duration

5 Days

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