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We're here to help you find itDiscover the power of GPU acceleration with our Accelerating Data Science Workflows (NVIDIA) course, designed for developers keen on speeding up their data science projects. In just 8 hours, you'll learn to build and execute end-to-end GPU-accelerated workflows that improve your productivity and efficiency.
The course covers GPU-accelerated data preparation with cuDF and Apache Arrow, machine learning tasks using XGBoost and cuML, graph analysis with cuGraph, and creating stunning data visualizations using cuXFilter. You'll apply these concepts to real-world scenarios, enabling fast data manipulation and analysis. Perfect for those in software, finance, or retail industries, this course will enhance your ability to make informed business decisions.
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
To ensure a successful learning experience in the Accelerating End-to-End Data Science Workflows (NVIDIA) course, participants should meet the following minimum required prerequisites:
Basic Knowledge of Python Programming: Understand fundamental concepts and syntax of Python programming to implement and experiment with various data science techniques.
Familiarity with Data Science Concepts: Have a basic understanding of key data science concepts such as data preparation, feature extraction, and machine learning algorithms.
Experience with Data Analysis Tools: Be comfortable using data analysis tools and libraries such as Pandas and NumPy, which will aid in transitioning to GPU-accelerated libraries like cuDF.
Understanding of Machine Learning: Basic knowledge of machine learning algorithms and concepts, such as supervised and unsupervised learning, as well as experience applying machine learning techniques to datasets.
With these prerequisites, participants will be well-prepared to leverage the power of GPU-acceler
1. Introduction: Accelerating End-to-End Data Science Workflows (NVIDIA) empowers developers to optimize data science tasks using GPU acceleration, drastically enhancing productivity and performance.
2. Job Roles and Audience:
Introduction The NVIDIA Accelerating End-to-End Data Science Workflows course equips developers with skills to build and execute GPU-accelerated data science workflows, enabling quick exploration, iteration, and production deployment using RAPIDS libraries.
Learning Objectives and Outcomes
Implement GPU-accelerated data preparation and feature extraction
Apply a broad spectrum of GPU-accelerated machine learning tasks
Execute GPU-accelerated graph analysis
Build data visualizations
Upon completion, participants will be able to:
