https://github.com/hrolive/fundamentals-of-accelerated-data-science

How to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.
https://github.com/hrolive/fundamentals-of-accelerated-data-science

accelerated-computing bokeh cudf cugraph cuml cupy dask data-science jupyter numpy pandas python rapids xgboost

Last synced: about 2 months ago
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How to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.

• Host: GitHub
• URL: https://github.com/hrolive/fundamentals-of-accelerated-data-science
• Owner: HROlive
• Created: 2025-03-10T18:50:28.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-04-22T16:14:53.000Z (6 months ago)
• Last Synced: 2025-04-22T17:33:44.119Z (6 months ago)
• Topics: accelerated-computing, bokeh, cudf, cugraph, cuml, cupy, dask, data-science, jupyter, numpy, pandas, python, rapids, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 33.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
![Course](images/banner.png)

## Table of Contents

1. [Description](#description)

2. [Information](#information)

3. [Certificate](#certificate)



## Description

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.



## Information

Upon completion, you will be able to perform various data science tasks more efficiently, enabling more iteration cycles and drastically improving productivity:

> - 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.

More detailed information and links for the course can be found on the [course website](https://learn.nvidia.com/courses/course?course_id=course-v1:DLI+C-DS-02+V2&unit=block-v1:DLI+C-DS-02+V2+type@vertical+block@ab89a641ed8f4e69933874b1d09e5368).



## Certificate

The certificate for the course can be found below:

- ["Fundamentals of Accelerated Data Science" - NVIDIA Deep Learning Institute](https://learn.nvidia.com/certificates?id=rhS5B6lyTHKCp3bQjFNW3A) (Issued On: July 2025)