Accelerate Data Science Workflows with Zero Code Changes Course Overview

Accelerate Data Science Workflows with Zero Code Changes Course Overview

Accelerate Data Science Workflows with Zero Code Changes

This 4-hour course on Accelerate Data Science Workflows with Zero Code Changes is designed for data scientists familiar with data processing and Python libraries. By leveraging NVIDIA RAPIDS, you can GPU-accelerate your existing data science tasks without altering your code. The course highlights the benefits of a unified workflow across CPUs and GPUs, enabling quicker and more efficient data handling. Participants will witness a significant reduction in processing time, enhancing productivity and efficiency. By the end, you will be adept at enhancing various data processing and machine learning workflows.

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575

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Course Fee 575
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575 (USD)
  • Live Training (Duration : 4 Hours)
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  • Live Training (Duration : 4 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

Prerequisites for Accelerate Data Science Workflows with Zero Code Changes

To ensure a successful learning experience in the "Accelerate Data Science Workflows with Zero Code Changes" course offered by Koenig Solutions, it is recommended that participants have:


  • Basic understanding of data processing and knowledge of a standard data science workflow on tabular data: Familiarity with data handling and the steps involved in a typical data science project will help you grasp course concepts more efficiently.
  • Experience using common Python libraries for data analytics: Practical experience with Python libraries such as Pandas, NumPy, or Scikit-Learn will make it easier to understand and apply GPU acceleration techniques taught in this course.

These prerequisites are designed to ensure that learners can fully benefit from the training and are not overwhelmed by the course content.


Target Audience for Accelerate Data Science Workflows with Zero Code Changes

Accelerate Data Science Workflows with Zero Code Changes is a 4-hour course designed for data professionals aiming to leverage GPU-acceleration using NVIDIA RAPIDS for faster data processing and machine learning workflows.


  • Data Scientists
  • Data Analysts
  • Machine Learning Engineers
  • AI Engineers
  • Data Engineers
  • Research Scientists
  • BI Analysts
  • IT Managers overseeing data science teams
  • Python Developers with a focus on data analytics
  • Computational Scientists
  • Quantitative Analysts


Learning Objectives - What you will Learn in this Accelerate Data Science Workflows with Zero Code Changes?

Introduction to the Course's Learning Outcomes and Concepts Covered

Accelerate Data Science Workflows with Zero Code Changes is a 4-hour course designed to teach data scientists how to use NVIDIA RAPIDS for GPU acceleration in their existing workflows, leading to faster data processing and increased efficiency.

Learning Objectives and Outcomes

  • Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks.
  • Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes.
  • Experience the significant reduction in processing time when workflows are GPU-accelerated.
  • Master the fundamentals of NVIDIA RAPIDS and its application in real-world scenarios.
  • Gain hands-on experience in deploying RAPIDS for existing data science workflows.
  • Explore case studies that demonstrate the application and benefits of RAPIDS in industry settings.
  • Develop the skills to benchmark and compare traditional CPU-based workflows with GPU-accelerated alternatives.
  • Learn how to integrate RAPIDS with popular Python data analytics libraries seamlessly.
  • Understand best practices for optimizing data science pipelines using GPU acceleration.
  • Foster a comprehensive understanding of the tools and techniques required to maximize productivity and efficiency in data science tasks.

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