{"id":101594,"date":"2026-08-12T09:29:49","date_gmt":"2026-08-12T03:59:49","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=101594"},"modified":"2026-08-12T09:29:49","modified_gmt":"2026-08-12T03:59:49","slug":"nvidia-accelerated-data-science-curriculum","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/","title":{"rendered":"NVIDIA Accelerated Data Science Curriculum: Full Breakdown"},"content":{"rendered":"<!--[if lt IE 9]><script>document.createElement('audio');<\/script><![endif]-->\n<audio class=\"wp-audio-shortcode\" id=\"audio-101594-1\" preload=\"none\" style=\"width: 100%;\" controls=\"controls\"><source type=\"audio\/mpeg\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nca-ads-curriculum.mp3?_=1\" \/><a href=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nca-ads-curriculum.mp3\">https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nca-ads-curriculum.mp3<\/a><\/audio>\n<p><span style=\"font-weight: 400;\">You look at the <\/span><b>NVIDIA-accelerated data science curriculum<\/b><span style=\"font-weight: 400;\">, expecting answers to all your questions. Instead, you find a list of topics to cover, including GPU acceleration, RAPIDS, cuDF, XGBoost, and data processing. The words sound impressive, but they don\u2019t really answer what you will be learning every day.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2192 Will you be spending hours cramming through the notes or actually building something?<br \/>\n<\/span><span style=\"font-weight: 400;\">\u2192 Will you learn about new tools or understand how data scientists use them to solve real problems?<br \/>\n<\/span><span style=\"font-weight: 400;\">\u2192 What skills will you genuinely build, and how will they support your career growth?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Most course pages don\u2019t answer these questions, but this NVIDIA Accelerated Data Science Curriculum guide does. It is not a repeated version of the NVIDIA data science course syllabus but the <\/span><b>hands-on skills you will develop to prepare<\/b><span style=\"font-weight: 400;\"> for real-world projects.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#NVIDIA_Accelerated_Data_Science_Curriculum_Overview\" >NVIDIA Accelerated Data Science Curriculum Overview<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#What_Will_You_Learn_in_the_NVIDIA_Accelerated_Data_Science_Curriculum\" >What Will You Learn in the NVIDIA Accelerated Data Science Curriculum?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Key_Skills_Youll_Build_During_the_NVIDIA_Data_Science_Course\" >Key Skills You&#8217;ll Build During the NVIDIA Data Science Course<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Who_Should_Take_the_NVIDIA_Accelerated_Data_Science_Course\" >Who Should Take the NVIDIA Accelerated Data Science Course?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Practical_Learnings_In_NVIDIA_Data_Science_Certification_Preparation\" >Practical Learnings In NVIDIA Data Science Certification Preparation<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Build_an_End-to-End_GPU-Accelerated_Data_Science_Workflow\" >Build an End-to-End GPU-Accelerated Data Science Workflow<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Train_and_Optimise_Machine_Learning_Models_Using_NVIDIA_GPUs\" >Train and Optimise Machine Learning Models Using NVIDIA GPUs<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#How_NVIDIA_Accelerated_Data_Science_Differs_from_Traditional_Data_Science_Courses\" >How NVIDIA Accelerated Data Science Differs from Traditional Data Science Courses<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#What_Can_You_Do_After_Completing_the_NVIDIA_Accelerated_Data_Science_Course\" >What Can You Do After Completing the NVIDIA Accelerated Data Science Course?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Build_Faster_GPU-Accelerated_Data_Science_Workflows\" >Build Faster GPU-Accelerated Data Science Workflows<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Master_the_NVIDIA_RAPIDS_and_GPU_Data_Science_Ecosystem\" >Master the NVIDIA RAPIDS and GPU Data Science Ecosystem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Apply_GPU_Data_Science_Skills_to_Real_AI_Projects\" >Apply GPU Data Science Skills to Real AI Projects<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#How_to_Prepare_for_the_NVIDIA_Accelerated_Data_Science_Certification_Exam\" >How to Prepare for the NVIDIA Accelerated Data Science Certification Exam<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#NVIDIA_Accelerated_Data_Science_Curriculum_FAQs\" >NVIDIA Accelerated Data Science Curriculum FAQs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.whizlabs.com\/blog\/nvidia-accelerated-data-science-curriculum\/#Final_Thoughts_on_the_NVIDIA_Accelerated_Data_Science_Curriculum\" >Final Thoughts on the NVIDIA Accelerated Data Science Curriculum<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"NVIDIA_Accelerated_Data_Science_Curriculum_Overview\"><\/span><b>NVIDIA Accelerated Data Science Curriculum Overview<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-101598\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview.webp\" alt=\"nvidia accelerated data science curriculum overview\" width=\"1678\" height=\"1385\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview.webp 1678w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview-300x248.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview-1024x845.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview-768x634.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview-1536x1268.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/nvidia-accelerated-data-science-curriculum-overview-150x124.webp 150w\" sizes=\"(max-width: 1678px) 100vw, 1678px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Most course pages explicitly cover the <\/span><b>NVIDIA data science course modules<\/b><span style=\"font-weight: 400;\"> and learning objectives. But what about seeing those technologies in practice and what you will be able to do by the end of your course?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The <\/span><b>NVIDIA data science learning path<\/b><span style=\"font-weight: 400;\"> focuses on how GPU tools speed up model training and the skills you will develop.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Let\u2019s look at what you should know before enrolling in the course and what to expect out of it.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_Will_You_Learn_in_the_NVIDIA_Accelerated_Data_Science_Curriculum\"><\/span><b>What Will You Learn in the NVIDIA Accelerated Data Science Curriculum?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-101599\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads.webp\" alt=\"what you will learn in the nca-ads\" width=\"1678\" height=\"1059\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads.webp 1678w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads-300x189.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads-1024x646.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads-768x485.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads-1536x969.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2026\/08\/what-you-will-learn-in-the-nca-ads-150x95.webp 150w\" sizes=\"(max-width: 1678px) 100vw, 1678px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">If you initially look at the <\/span><b>accelerated data science training content<\/b><span style=\"font-weight: 400;\">, it will seem like a collection of jargon. But when you start diving deeper into the course, you will realise that it focuses on a complete data science workflow rather than on individual tools.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You will not be learning about RAPIDS, cuDF, Jupyter, or XGBoost in isolation, but how they complement each other to increase workflow efficiency.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, you can use Jupyter to write the codes and cUDF to prepare the data. Afterwards, XGBoost helps train machine learning models using the same data, and RAPIDS helps move the data to model training. At the end, they all are coming together to design an efficient data science model for your company.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By the end of the course, you will have a better understanding of how GPU-accelerated data science works in practical business scenarios.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Key_Skills_Youll_Build_During_the_NVIDIA_Data_Science_Course\"><\/span><b>Key Skills You&#8217;ll Build During the NVIDIA Data Science Course<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The <\/span><b>NVIDIA data science course syllabus <\/b><span style=\"font-weight: 400;\">builds your confidence in leveraging GPU tools for real tasks. As you break down every module, you will find the following skills:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handling large datasets, scaling the workload of <\/span><b>big data processing with GPU <\/b><span style=\"font-weight: 400;\">instead of depending on CPU-based workflows.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learning your way around the <\/span><b>RAPIDS ecosystem <\/b><span style=\"font-weight: 400;\">and cuDF to analyse large datasets more efficiently.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Using XGBoost, a GPU-powered framework to train machine learning models quicker.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Focusing on end-to-end <\/span><b>data science workflow optimisation<\/b><span style=\"font-weight: 400;\">, from data collection to model development.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Using Jupyter Notebook, an interactive workspace to write Python code and analyse data.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By the end of the course, you will be proficient in identifying how different NVIDIA technologies support professional data science workflows.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Who_Should_Take_the_NVIDIA_Accelerated_Data_Science_Course\"><\/span><b>Who Should Take the NVIDIA Accelerated Data Science Course?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The <\/span><b>NVIDIA RAPIDS curriculum <\/b><span style=\"font-weight: 400;\">is suitable for learners who have a strong understanding of Python, data science, analysis, and machine learning. So, the focus is not on teaching you the concepts again, but on explaining how NVIDIA&#8217;s GPU-enabled technologies help you perform tasks more efficiently.<\/span><\/p>\n<p><strong>It is a particularly valuable course for the following professionals:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data scientists looking to speed up data processing and model training.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Machine learning engineers who want to optimise their AI and ML workflows using GPUs.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI practitioners interested in building faster, more scalable machine learning solutions.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data analysts ready to move beyond traditional CPU-based data processing.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Software developers exploring GPU computing for data science and AI applications.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Students and researchers who already understand the fundamentals of Python and machine learning and want hands-on experience with NVIDIA&#8217;s data science ecosystem.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This curriculum is designed to help learners improve their existing data science skills. But if you are entirely new to Python and machine learning concepts, then it is ideal to build the fundamental knowledge first.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Practical_Learnings_In_NVIDIA_Data_Science_Certification_Preparation\"><\/span><b>Practical Learnings In NVIDIA Data Science Certification Preparation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">If you focus only on <\/span><b>what is covered in the NVIDIA data science course <\/b><span style=\"font-weight: 400;\">and do not aim for hands-on experience, you will fall behind. During the course, you will understand what the NVIDIA-accelerated tools do and learn how to apply them to enterprise tasks. Gradually, you will understand how the workflow progresses to solve the business problems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Below are two practical skills you will adopt while covering the <\/span><b>NVIDIA data science course structure<\/b><span style=\"font-weight: 400;\">:<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Build_an_End-to-End_GPU-Accelerated_Data_Science_Workflow\"><\/span><b>Build an End-to-End GPU-Accelerated Data Science Workflow<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">One of the initial skills you will adopt is learning how a typical data science workflow becomes more efficient with the integration of a GPU. You will do more than just understand what NVIDIA tools do. You will use them to complete the tasks expected from a data scientist when working with a huge dataset.<\/span><\/p>\n<p><strong>As a part of this NVIDIA RAPIDS curriculum, you will do the following:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Import and prepare datasets for analysis using GPU-accelerated tools.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Clean and transform data to make it suitable for machine learning workflows.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Process large datasets more efficiently by using GPU computing instead of relying solely on CPUs.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understand how each stage connects, including data ingestion, preparation, analysis, and model development.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By the end of your course, you will have practical knowledge of how to leverage GPUs to process data and solve business problems quickly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Learn more about how to deploy an end-to-end data processing pipeline <\/span><a href=\"https:\/\/learn.nvidia.com\/courses\/course-detail?course_id=course-v1:DLI+C-DS-04+V1\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">here<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Train_and_Optimise_Machine_Learning_Models_Using_NVIDIA_GPUs\"><\/span><b>Train and Optimise Machine Learning Models Using NVIDIA GPUs<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">What you will learn next is building and improving how machine learning models function. It focuses on teaching how the NVIDIA-enabled tools speed up model training and improve ML workflow efficiency.<\/span><\/p>\n<p><strong>During this stage of the curriculum, you will focus on the following:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Train machine learning models using GPU frameworks such as XGBoost.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Compare model performance to understand how GPUs reduce processing time and improve training efficiency for compatible workloads.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Experiment with optimisation techniques to improve model performance and reduce training time.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Work through practical exercises that demonstrate how accelerated computing supports real-world AI and data science projects.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By the end of your studies, you will be confident about using NVIDIA technologies to speed up the workflow and improve business project outcomes.<\/span><\/p>\n<p><strong>Along with the above-discussed subjects, you will also learn the following:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Detecting anomalies in a time-series dataset.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Performing exploratory data analysis (EDA).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integrating data caching to minimise shuffle.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Monitoring data processing pipelines to identify bottlenecks.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimising hyperparameters for machine learning models.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Comparing single-GPU and multi-GPU scenarios to train machine learning models.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Comparing the required memory with the available memory on a device.<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"How_NVIDIA_Accelerated_Data_Science_Differs_from_Traditional_Data_Science_Courses\"><\/span><b>How NVIDIA Accelerated Data Science Differs from Traditional Data Science Courses<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Did you feel that this curriculum is just another <\/span><b>NVIDIA data science course outline<\/b><span style=\"font-weight: 400;\">? The objective of this certification is not to teach you how to build machine learning models. It assumes that you are already proficient in it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The focus of this certification is to understand how to optimise the model and make it more efficient using GPU-enabled tools.<\/span><\/p>\n<p><strong>It shifts your learning approach in the following ways:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">You&#8217;re improving existing skills, not starting from scratch. The curriculum builds on your knowledge of Python and data science instead of introducing these concepts.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The focus is on both performance and functionality. You&#8217;ll learn how to complete common data science tasks and the way NVIDIA technologies can help process larger datasets within a time crunch.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">You will work with the tools used in modern AI workflows. Rather than learning about the technologies separately, you&#8217;ll understand how tools like RAPIDS, cuDF, Jupyter, and machine learning frameworks fit into a complete workflow.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The emphasis is on applying concepts. Through hands-on exercises, you&#8217;ll explore how quick computing supports practical AI and data science projects.<\/span><\/li>\n<\/ul>\n<p><strong>The NVIDIA-certified professional accelerated data science certification is the best choice if you want to advance your skills. It will cover the following topics:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data analysis<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data manipulation and software literacy<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data preparation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">GPU and cloud computing<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Machine learning\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">MLOps<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"What_Can_You_Do_After_Completing_the_NVIDIA_Accelerated_Data_Science_Course\"><\/span><b>What Can You Do After Completing the NVIDIA Accelerated Data Science Course?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">By the time you cover the curriculum, your knowledge will exceed the basic understanding of NVIDIA tools. You will know how to leverage the tools to streamline the workflow and boost productivity. It will also make you proficient in identifying the bottlenecks and making changes to your plan accordingly.<\/span><\/p>\n<p><strong>After covering the NVIDIA data science course structure, you will be able to:<\/strong><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Build_Faster_GPU-Accelerated_Data_Science_Workflows\"><\/span><b>Build Faster GPU-Accelerated Data Science Workflows<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">You will be proficient in using GPUs to streamline the following tasks:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data preparation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data transformation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ML model training<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data cleansing<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">You no longer have to depend on CPU-based processing and can leverage the technology to boost workflow productivity.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Master_the_NVIDIA_RAPIDS_and_GPU_Data_Science_Ecosystem\"><\/span><b>Master the NVIDIA RAPIDS and GPU Data Science Ecosystem<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>The curriculum familiarises you with the following technologies:<\/strong><\/p>\n<ul>\n<li aria-level=\"1\">RAPIDS ecosystem<\/li>\n<li aria-level=\"1\">CUDA-accelerated machine learning<\/li>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Graph data analysis tools like cuGraph<\/span><\/li>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Management frameworks like Docker and Conda<\/span><\/li>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Memory-optimisation techniques like batching and mixed precision<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">You learn how these technologies work cohesively to improve workflow.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Apply_GPU_Data_Science_Skills_to_Real_AI_Projects\"><\/span><b>Apply GPU Data Science Skills to Real AI Projects<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">You learn to apply all the concepts that you learn in the <\/span><b>NVIDIA data science course modules <\/b><span style=\"font-weight: 400;\">for the following:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Processing and analysing large datasets<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Training and optimising machine learning models<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Improving data processing pipelines<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Supporting AI applications that require high-performance computing<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Continue Learning With A Strong Foundation<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The biggest lesson here is to build your confidence. Once you know how the GPU fits into the modern data science workflow, you can explore more advanced NVIDIA technologies and choose a specialisation certification.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_Prepare_for_the_NVIDIA_Accelerated_Data_Science_Certification_Exam\"><\/span><a href=\"https:\/\/www.whizlabs.com\/blog\/how-to-pass-nvidia-ncp-ads-exam\/\"><b>How to Prepare for the NVIDIA Accelerated Data Science Certification Exam<\/b><\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">When it comes to a certification like NVIDIA Accelerated Data Science, the focus is more on skill building than theoretical knowledge. However, it is critical to take your time and leverage both theory and practice to build confidence.<\/span><\/p>\n<p><b>Here are some of the most effective study resources to prepare you for the exam:<\/b><\/p>\n<ul>\n<li aria-level=\"1\"><b>NVIDIA Developer documentation on RAPIDS: <\/b><span style=\"font-weight: 400;\">Browse through a collection of <\/span><a href=\"https:\/\/docs.nvidia.com\/rapids\/index.html\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">libraries<\/span><\/a><span style=\"font-weight: 400;\"> where you learn how to run data science pipelines on the GPU. You can install guides or user manuals or browse through the RAPIDS release notes for learning.<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Practice Tests: <\/b><span style=\"font-weight: 400;\">Once you are confident in your preparation, attempt full-length <\/span><a href=\"https:\/\/www.whizlabs.com\/nvidia-professional-accelerated-data-science-course\/\"><b>NCP-ADS practice tests<\/b><\/a><span style=\"font-weight: 400;\"> to assess your readiness. These tests will help you identify your strengths and weaknesses and improve your time management skills.<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"NVIDIA_Accelerated_Data_Science_Curriculum_FAQs\"><\/span><b>NVIDIA Accelerated Data Science Curriculum FAQs<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><b>1. What are the prerequisites for the NCP accelerated data science certification?<br \/>\n<\/b><span style=\"font-weight: 400;\">Although there are no compulsory certification requirements, the candidates are expected to have at least two to three years of experience in GPU-accelerated computing and machine learning.<\/span><\/p>\n<p><b>2. Is the NVIDIA data science learning path suitable for beginners?<br \/>\n<\/b><span style=\"font-weight: 400;\">No, the NVIDIA accelerated data science exam is an associate-level exam and is only suitable for those with a strong foundation in GPU acceleration.<\/span><\/p>\n<p><b>3. What is the NVIDIA professional accelerated data science certification exam cost?<br \/>\n<\/b><span style=\"font-weight: 400;\">The exam cost for the NCP-ADS certification is $200 USD.<\/span><\/p>\n<p><b>4. Which skills are validated in the NCP-ADS certification exam?<br \/>\n<\/b><span style=\"font-weight: 400;\">The NVIDIA associate-level certification validates your understanding of processing GPU-based data, machine learning skills on GPUs, and Ace Graph analytics. It also assesses your ability to work on high-performing data ingestion and ETL, large dataset visualisation, workflow execution, end-to-end data science, and troubleshooting.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Final_Thoughts_on_the_NVIDIA_Accelerated_Data_Science_Curriculum\"><\/span><b>Final Thoughts on the NVIDIA Accelerated Data Science Curriculum<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The <\/span><b>NVIDIA data science course syllabus <\/b><span style=\"font-weight: 400;\">covers more than just understanding how to build a model. It is about optimising the models to boost workflow and end results. But going through the curriculum randomly might not prepare you effectively or clear your concepts. You must focus on skill building rather than continuously revising the syllabus. Go through online videos, official documents, hands-on lab trials, and practice papers so you are fully prepared.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you would like to explore the full curriculum and prerequisites in detail, check out <\/span><strong><a href=\"https:\/\/www.whizlabs.com\/nvidia-professional-accelerated-data-science-course\/\">Whizlabs\u2019 NVIDIA-professional accelerated data science course<\/a><\/strong><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>You look at the NVIDIA-accelerated data science curriculum, expecting answers to all your questions. Instead, you find a list of topics to cover, including GPU acceleration, RAPIDS, cuDF, XGBoost, and data processing. The words sound impressive, but they don\u2019t really answer what you will be learning every day. \u2192 Will you be spending hours cramming through the notes or actually building something? \u2192 Will you learn about new tools or understand how data scientists use them to solve real problems? \u2192 What skills will you genuinely build, and how will they support your career growth? Most course pages don\u2019t answer [&hellip;]<\/p>\n","protected":false},"author":441,"featured_media":101597,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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Subramanian","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/prabhu-subramanian\/"},"uagb_comment_info":0,"uagb_excerpt":"You look at the NVIDIA-accelerated data science curriculum, expecting answers to all your questions. Instead, you find a list of topics to cover, including GPU acceleration, RAPIDS, cuDF, XGBoost, and data processing. The words sound impressive, but they don\u2019t really answer what you will be learning every day. \u2192 Will you be spending hours cramming&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/101594","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/441"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=101594"}],"version-history":[{"count":5,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/101594\/revisions"}],"predecessor-version":[{"id":101603,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/101594\/revisions\/101603"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/101597"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=101594"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=101594"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=101594"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}