Differences

This shows you the differences between two versions of the page.

--- training:ds:2025:accelerated_ds [2025/11/11 12:06] – Didier Barradas Bautista
+++ training:ds:2025:accelerated_ds [2025/12/04 10:54] (current) – Didier Barradas Bautista
@@ Line 1: / Line 1: @@
-Here’s a vibrant and **eye-catching Docuwiki page** for the upcoming **NVIDIA DLI Workshop** based on your uploaded flyer:
+====== Fundamentals of Accelerated Data Science (NVIDIA DLI Workshop) ======
-***
+<WRAP group> <WRAP quarter column> <WRAP center round box 110%> {{:icon:twbs:link:w-25:h-auto:calendar3.svg?nolink&}} <tab1> <fs:26px>** Date**</fs>
-```dokuwiki
+  * Sunday, December 7, 2025
-====== ⚡ NVIDIA DLI Workshop: Fundamentals of Accelerated Data Science ======
+  * 9:00 am – 1:00 pm
-===== 🗓️ Date & Time =====
+<wrap indent></wrap> \\
-  * **Sunday, December 7, 2025**
+</WRAP> </WRAP>
-  * **9:00 am – 1:00 pm**
-===== 📍 Location =====
+<WRAP quarter column> <WRAP center round box 110%> {{:icon:twbs:link:w-25:h-auto:map.svg?nolink&}} <tab1> <fs:26px>** Venue**</fs>
-  * Building 1, Level 2, Multi-purpose Room (MPR) – Desert Side
-===== 🧑‍🏫 Hosted By =====
+  * Building 1, Level 2, Multi-purpose Room (MPR) (Desert Side)
-  * [[https://wiki.vis.kaust.edu.sa/|KAUST Visualization Core Lab]]
-  * [[https://www.kaust.edu.sa/en/research/generative-ai|KAUST Generative AI Center of Excellence]]
-  * [[https://www.nvidia.com/en-us/training/|NVIDIA Deep Learning Institute (DLI)]]
-===== 🧠 Workshop Overview =====
+<wrap indent></wrap> \\
-Join us for a **hands-on training** session where you'll learn how to use **GPU-accelerated tools** to supercharge your data science workflows. This workshop is designed to help you achieve **scalable, reliable, and cost-effective** results.
+<wrap indent></wrap> \\
+</WRAP> </WRAP>
-**Workshop Title**: Fundamentals of Accelerated Data Science
+<WRAP quarter column>
-**Audience**: KAUST students, staff, and researchers (academic verification required)
+<WRAP center round box 110%> {{:icon:twbs:link:w-25:h-auto:globe.svg?nolink&}} <tab1> <fs:26px>** Organizer**</fs>
-===== 🚀 What You'll Learn =====
+{{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Didier Barradas Bautista \\
-  * Use **cuDF** to accelerate pandas, Polars, and Dask for large-scale data analysis
+{{:icon:twbs:link:w-25:h-auto:headset-vr.svg?nolink&}} Visualization Core Laboratory \\
-  * Apply **XGBoost** and other ML algorithms to real-world problems
+{{:icon:twbs:link:w-25:h-auto:envelope-at.svg?nolink&}} didier.barradasbautista@kaust.edu.sa
-  * Analyze complex networks using **NetworkX** and **cuGraph**
-===== 🧰 Prerequisites =====
+</WRAP> </WRAP>
-  * Experience with Python (especially pandas and NumPy)
-  * A laptop with the latest version of Chrome or Firefox
-===== 🏆 Certification =====
+<WRAP quarter column> <WRAP center round box 100%>{{:icon:twbs:link:w-25:h-auto:person-add.svg?nolink&}} <tab1> <fs:26px>** Register**</fs>
-  * **Code-based assessment**
-  * **Certificate available upon completion**
-===== 🔗 Learn More =====
+<wrap em button> [[https://kaustforms.formstack.com/forms/accelerated_ds_25_fall |Register Here]] </wrap> \\
-  * [[https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-DS-02+V2|Course Details on NVIDIA Learn]]
+<wrap indent></wrap> \\
+<wrap indent></wrap> \\
+<wrap indent></wrap> \\
+</WRAP> </WRAP> </WRAP>
-===== 📝 Register Now =====
+<WRAP group> <WRAP third column>
-  * [[https://kaustforms.formstack.com/forms/accelerated_ds_25_fall|**Click here to register**]]
+{{training:ds:2025:nvidia-logo-vert.png}}
-===== ❓ Questions? =====
+<WRAP center round box download 100%>
-  * Email: didier.barradasbautista@kaust.edu.sa
+ <fs:26px>** Workshop Materials**</fs>
-  * Support: [[mailto:help@vis.kaust.edu.sa|help@vis.kaust.edu.sa]]
+  * Learn more: [[https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-DS-02+V2|Course Page]]
+  * NVIDIA DLI: [[https://learn.nvidia.com/join/|Training Portal]]
+  * NVIDIA event site: [[https://learn.nvidia.com/dli-event|Event Portal]]
-----
+</WRAP>
-{{tag>workshop nvidia ai data-science gpu accelerated-learning}}
+<WRAP center round box todo 100%>
-```
+ <fs:26px>** How to Prepare?**</fs>
-***
+  * Bring a laptop with the latest version of Chrome or Firefox
+  * Review Python basics, especially pandas and NumPy
+  * Familiarize yourself with [[https://developer.nvidia.com/rapids|RAPIDS]] and [[https://developer.nvidia.com/cugraph|cuGraph]]
+</WRAP>
+</WRAP>
+<WRAP twothirds column>
+===== Overview =====
+The NVIDIA Deep Learning Institute (DLI), in collaboration with the KAUST Visualization Core Lab and the KAUST Generative AI Center of Excellence, invites you to a hands-on workshop on **GPU-accelerated data science**.
+This session will teach you how to use NVIDIA tools to accelerate data workflows, improve scalability, and reduce time-to-insight. You’ll gain practical experience with cuDF, XGBoost, and cuGraph.
+This training is **exclusively for KAUST academic students, staff, and researchers**.
+===== Who Should Attend? =====
+This workshop is ideal for:
+  * Graduate students and postdocs
+  * Research staff and faculty
+  * Anyone working with large-scale data science problems
+===== Learning Objectives =====
+  * Use **cuDF** to accelerate pandas, Polars, and Dask
+  * Apply **XGBoost** and other ML algorithms
+  * Analyze networks using **NetworkX** and **cuGraph**
+===== Prerequisites =====
+  * Experience with Python (especially pandas and NumPy)
+  * A laptop with Chrome or Firefox installed
+===== Certification =====
+  * Code-based assessment
+  * Certificate available upon successful completion
+===== Agenda =====
+^ Time ^ Topic ^
+| 09:00 | Welcome & Introduction |
+| 09:15 | Accelerated DataFrames with cuDF |
+| 10:00 | Machine Learning with RAPIDS & XGBoost |
+| 11:00 | Graph Analytics with cuGraph |
+| 12:00 | Hands-on Exercises & Q&A |
+</WRAP> </WRAP>
+----
-Would you like this exported as a `.txt` file for upload to your wiki? Or would you like a matching flyer or slide deck to go with it?
+{{tag>workshop nvidia gpu data-science accelerated-learning}}