Accelerate Data Science Workflows with Zero Code Changes
by nvidia
Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.
By participating in this course, you will:
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
Prerequisites:
Basic understanding of data processing and knowledge of a standard data science workflow on tabular data
Experience using common Python libraries for data analytics
Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX
🆓 Free Online Course
⏰ Duration : More than 1 hour
🏃♂️ Self paced
✅ Certification available
Course Link
#datascience #nvidia
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
by nvidia
Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.
By participating in this course, you will:
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
Prerequisites:
Basic understanding of data processing and knowledge of a standard data science workflow on tabular data
Experience using common Python libraries for data analytics
Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX
🆓 Free Online Course
⏰ Duration : More than 1 hour
🏃♂️ Self paced
✅ Certification available
Course Link
#datascience #nvidia
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Python for Data Science with Assignments
A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc.
Rating ⭐️: 4.7 out 5
Students 👨🎓 : 18046
Duration ⏰ : 9.5 hours on-demand video
Created by 👨🏫: Meritshot Academy
🔗 Course Link
⚠️ Its free for first 1000 enrollments only!
#python #datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc.
Rating ⭐️: 4.7 out 5
Students 👨🎓 : 18046
Duration ⏰ : 9.5 hours on-demand video
Created by 👨🏫: Meritshot Academy
🔗 Course Link
⚠️ Its free for first 1000 enrollments only!
#python #datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Udemy
Python for Data Science with Assignments
A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc.
Practical Deep Learning For Coders
This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step onelearning how to get a GPU server online suitable for deep learningand go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.
🆓 Free Online Course
Rating⭐️: 4.1 out 5
Duration ⏰: 7 weeks
💻 Lecture Videos
🏃♂️ Self paced
Teacher 👨🏫 : Prof. Jeremy Howard
🔗 Course Link
#programming #deeplearning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step onelearning how to get a GPU server online suitable for deep learningand go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.
🆓 Free Online Course
Rating⭐️: 4.1 out 5
Duration ⏰: 7 weeks
💻 Lecture Videos
🏃♂️ Self paced
Teacher 👨🏫 : Prof. Jeremy Howard
🔗 Course Link
#programming #deeplearning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Free Video Lectures
Practical Deep Learning For Coders online course video lectures by Other
Practical Deep Learning For Coders free online course video tutorial by Other.You can download the course for FREE !
How To Use R Programming for Research
Use R Programming for Scientific Research
Rating ⭐️: 4.5 out 5
Students 👨🎓 : 19,897
Duration ⏰ : 1.5 hours on-demand video
👩💻 2 coding exercises
⬇️ 29 downloadable resources
Created by 👨🏫: Prof Asad Rasul
🔗 COURSE LINK
⚠️ Its free for first 1000 enrollments only!
#R_Programming
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
Use R Programming for Scientific Research
Rating ⭐️: 4.5 out 5
Students 👨🎓 : 19,897
Duration ⏰ : 1.5 hours on-demand video
👩💻 2 coding exercises
⬇️ 29 downloadable resources
Created by 👨🏫: Prof Asad Rasul
🔗 COURSE LINK
⚠️ Its free for first 1000 enrollments only!
#R_Programming
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
Udemy
R for Researchers: From Basics to Advanced Analysis
Master R Programming for Scientific Research
10 Best Practices for Data Science
The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.
And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.
Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons
🔗 Read More
#datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.
And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.
Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons
🔗 Read More
#datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈