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Fundamental of Accelerated Data Science

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

GK# 847013
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What You'll Learn
  • 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

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