{"course":{"productid":34490,"modality":6,"active":true,"language":"en","title":"Fundamentals of Accelerated Data Science","productcode":"FADS","vendorcode":"NV","vendorname":"Nvidia","fullproductcode":"NV-FADS","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/nvidia-fads","objective":"<ul>\n<li>Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames<\/li><li>Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms<\/li><li>Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time<\/li><li>Rapidly achieve massive-scale graph analytics using cuGraph routines<\/li><\/ul>","essentials":"<p>Experience with Python, ideally including pandas and NumPy.<\/p>\n<p>Suggested resources to satisfy prerequisites: Kaggle&#039;s pandas Tutorials, Kaggle&#039;s Intro to Machine Learning, Accelerating Data Science Workflows with RAPIDS<\/p>","outline":"<p><strong>Introduction<\/strong>\t\n<\/p>\n<ul>\n<li>Meet the instructor.<\/li><li>Create an account at courses.nvidia.com\/join<\/li><\/ul><p><strong>GPU-Accelerated Data Manipulation<\/strong>\t\n<\/p>\n<ul>\n<li>Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop:<ul>\n<li>Read data directly to single and multiple GPUs with cuDF and Dask cuDF.<\/li><li>Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.<\/li><\/ul><\/li><\/ul><p><strong>GPU-Accelerated Machine Learning<\/strong>\t\n<\/p>\n<ul>\n<li>Apply several essential machine learning techniques to the data that was prepared in the first section:<ul>\n<li>Use supervised and unsupervised GPU-accelerated algorithms with cuML.<\/li><li>Train XGBoost models with Dask on multiple GPUs.<\/li><li>Create and analyze graph data on the GPU with cuGraph.<\/li><\/ul><\/li><\/ul><p><strong>Project: Data Analysis to Save the UK<\/strong>\t\n<\/p>\n<ul>\n<li>Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the entire UK population:<ul>\n<li>Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.<\/li><li>Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.<\/li><\/ul><\/li><\/ul><p><strong>Assessment and Q&amp;A<\/strong><\/p>","summary":"<p>Learn how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS&trade;, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.<\/p>\n<p><em>Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.<\/em><\/p>","objective_plain":"- Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames\n- Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms\n- Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time\n- Rapidly achieve massive-scale graph analytics using cuGraph routines","essentials_plain":"Experience with Python, ideally including pandas and NumPy.\n\nSuggested resources to satisfy prerequisites: Kaggle's pandas Tutorials, Kaggle's Intro to Machine Learning, Accelerating Data Science Workflows with RAPIDS","outline_plain":"Introduction\t\n\n\n\n- Meet the instructor.\n- Create an account at courses.nvidia.com\/join\nGPU-Accelerated Data Manipulation\t\n\n\n\n- Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop:\n- Read data directly to single and multiple GPUs with cuDF and Dask cuDF.\n- Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.\nGPU-Accelerated Machine Learning\t\n\n\n\n- Apply several essential machine learning techniques to the data that was prepared in the first section:\n- Use supervised and unsupervised GPU-accelerated algorithms with cuML.\n- Train XGBoost models with Dask on multiple GPUs.\n- Create and analyze graph data on the GPU with cuGraph.\nProject: Data Analysis to Save the UK\t\n\n\n\n- Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the entire UK population:\n- Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.\n- Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.\nAssessment and Q&A","summary_plain":"Learn how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS\u2122, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.\n\nPlease note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.","skill_level":"Beginner","version":"1.0","duration":{"unit":"d","value":1,"formatted":"1 day"},"pricelist":{"List Price":{"US":{"country":"US","currency":"USD","taxrate":null,"price":500},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":500},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":500},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":500},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":500},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":420},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":500},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":690}}},"lastchanged":"2025-07-29T12:18:27+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/en\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course-schedule\/34490","source_lang":"en","source":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course\/nvidia-fads"}}