Accelerated Data Science
SpectrumAnalytics
About the Data Science Track
The MegaLeap Data Science Track is delivered in partnership with NVIDIA. This track is designed for people with some background in data and are interested in advancing their skills.
Learning Objectives
In this course, students will learn how to build and execute end-to-end GPU accelerated data science workflows that enable them to quickly explore, iterate, and get their work into production. Using the RAPIDS accelerated data science libraries, developers will apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale.
By participating in this course, you’ll learn how to:
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Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames.
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Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms.
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Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time.
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Build beautiful data visualizations with the GPU-accelerated cuXFilter.
Upon completion, participants will be able to load, manipulate, and analyze data orders of magnitude fast, enabling more iteration cycles and drastically improving productivity.
Course Prerequisites:
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Experience with the Python programming language.
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Familiarity with the use of Pandas dataframes.
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Familiarity with the use of the scikit-learn machine learning library.