Some popular Python libraries for data analytics, like Numpy, Pandas, Scikit-Learn, etc., usually work well if the dataset fits into the RAM on a single machine. When dealing with large datasets, it could be a challenge to work around memory constraints. This course introduces scalable and accelerated data analytics with Dask and RAPIDS. Dask provides a framework and libraries that can handle large datasets on a single multi-core machine or across multiple machines on a cluster. RAPIDS, on the other hand, can accelerate your data analytics by offloading analytics workloads to GPUs with less effort in code changes.

Level: Introductory

Length: Two 3-Hour Sessions (2 Days)

Format: Lecture + Hands-on

Prerequisites:

• Alliance Account
• Basic Python and Linux command line experience. :: Mon. June 10 ::
  09:00 to 12:00
  

:: Wed. June 12 ::

13:30 to 16:30

Registration link

Compute Ontario Summer School is a series of online courses on Advanced Research Computing, High Performance Computing, Research Data Management, and Research Software. It runs from June 3 to June 21, 2024. The courses are delivered each workday from 9:00am to 4:30pm (EDT) with a lunch break,  in two parallel streams. Pick-and-choose the course(s) you want to attend. Registration is free. Please register early as  courses have a limited capacity. The Summer School is jointly delivered by SHARCNET, SciNet, Centre for Advanced Computing, in collaboration with the Alliance and RDM experts from across Ontario and Canada.