SciNet Education Site

In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.  Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.

Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI.

This workshop explores various concise and useful constructs for working with bash shell. The goal is to improve your shell skills. Attending this class requires some basic GNU/Linux command line experience.

Format: Virtual

Time:  1:00 pm - 4:00 pm EST

Learn how to write bash scripts, use environment variables, how to control process, and much more. Requires some Linux basic command line experience.

Format: Virtual

Parallel programming in Python. We will cover subprocess, numexpr, multiprocessing, MPI, and other parallel-enabling python packages.

Format: Virtual

Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.

Prerequisites: Some Linux command line experience.  Python experience is strongly advised. 

Format: Virtual

In this session for the Alliance EDIA Champions, we will give an introduction to basic concepts of working on a national advanced research computing ("ARC") facility.  We will explain what resources are available and what benefits they may offer to you.  We'll then delve into how to get access and how to run computations on these supercomputers.
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Dans cette session, nous donnerons une introduction aux concepts de base liés à l'utilisation d'une installation nationale de calcul informatique de pointe (CIP). Nous expliquerons quelles ressources sont disponibles et quels avantages elles peuvent vous offrir. Nous aborderons ensuite comment y accéder et comment exécuter des calculs sur ces superordinateurs.

Container computing is gradually changing the way researchers are developing, sharing, and running software applications. Apptainer (formerly called Singularity) is gaining popularity in HPC for its performance, ease of use, portability,  and security. In this course, we will explore: what is a container, why use a container, and how to use and create one.

Format: Virtual

This week's colloquium: "Setting Up Compute Infrastructure for Sensitive Data" by Yohai Meiron and Shawn Winnington-Ball from SciNet. 

The Compute Ontario Colloquia are weekly Zoom presentations on Advanced Research Computing, High Performance Computing, Research Data Management, and Research Software topics, delivered by staff from three Compute Ontario consortia (CAC, SciNet, SHARCNET) and guest speakers.  The colloquia are one hour long and include time for questions. No enrollment or registration is required. Most presentations are recorded and uploaded to the hosting consortium video channel.

New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.

Format: In-person. Sessions will be recorded.

Learn how to fully utilize the power of HPC. Discover proven strategies and tools to efficiently scale up from serial jobs to parallel runs across many compute nodes on Niagara.

Did you know the Linux operating system has built-in tools to control which specific users and groups can access which files and directories?  In this session, you will learn what these Linux permissions are, how to use the available tools to control access and sharing, and how to avoid common security pitfalls.

An introduction to basic concepts in High-Performance Computing (HPC).  This is intended to be a high-level primer for those largely new to HPC.  Topic will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism and a high-level overview of parallel programming models.

Format: Virtual

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.

Classes will be held Tuesdays and Thursdays, 10:00-11:30am, in VC323.

Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.

This course will provide an introduction to the theory, formalisms and algorithms of quantum computing.  The programming language will be Python; experience with Python will be assumed.  Experience with quantum mechanics is not necessary; an introductory-level understanding of linear algebra will be assumed.

This will be an in-person course, July 8 - 12, 12:00 - 3:00pm.

This course has been cancelled due to lack of attendance.