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.

There are three ways to follow this course:

• As a graduate course for UofT PhD and MSc students. Students that wish to do so, should not self-enrol here, but enrol using ACORN/ROSI.
• As a SciNet or Alliance user taking the course for SciNet certificate credits; for that, enrol on this site (note: limited spots).
• As an auditor following along with recordings but not submitting assignments; for this no enrollment is needed.

On a shared, remote resource like the Trillium supercomputer, running Python computation with specific packages requires some care.  The "virtual environment" approach is the most suitable for a supercomputer, but there are a few tricks to get it to work.  We will explain how these virtual environments work, and why they are better suited than other options like conda.   We will also show how to incorporate these virtual environments into the Open Ondemand web interface that is attached to the Trillium system.  

Format: Virtual

Confused on how to get the most out of the new Trillium supercomputer which replaces Niagara and Mist? Come visit our live "Trillium Office Hours", every Thursday in September at noon (EDT). Intended for all current and potential Trillium users. Bring your general or specific questions, and get immediate support from SciNet HPC analysts.  Format: In-person and on-line.

Confused on how to get the most out of the new Trillium supercomputer which replaces Niagara and Mist? Come visit our live "Trillium Office Hours", every Thursday in September at noon (EDT). Intended for all current and potential Trillium users. Bring your general or specific questions, and get immediate support from SciNet HPC analysts.  Format: In-person and on-line.

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 10:00-11:30am in ESB142, and Thursdays 12:00-1:30pm in MS2173.

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 week's colloquium: "What is Compute Ontario Summer School and how do I attend?" by Ann Allan from Compute Ontario. 

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.

• It’s free!
• All courses delivered online
• Pick-and-choose the course(s) you want to attend
• Many courses include hands-on components
• Courses range in length from 1.5 hours to three days
• Course levels range from beginner to advanced
• Topics covered include:  AI, machine learning, bioinformatics, GPU programming,  advanced research computing basics, high-performance computing tools, programming languages, neuroanalytics, visualization, research data management, and more.

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

Format: Virtual

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

Working with many of the HPC systems (like those at SciNet) involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course which will cover basic commands. It could be a great boon for your productivity.

Format: Virtual

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

This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).

This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.

This is an in-person course.