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| January,2023 | |
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10 Jan 10:30 am 12:00 pmEES1137 Lecture 1In 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. | EES1137 - Winter 2023 |
10 Jan 11:00 am 12:00 pmScientific Computing Lecture (PHY1610)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. | PHY1610 - Winter 2023 |
11 Jan 10:00 am 11:30 amIntro to SciNet, Niagara and MistIn about 90 minutes, learn how to use the SciNet systems Niagara and Mist, from securely logging in to running computations on the supercomputer. Experienced users may still pick up some valuable pointers.Format: In-person as well as broadcast and recorded. SciNet Teaching Room | HPC105 - Jan 2023 |
11 Jan 12:00 pm 1:00 pmCO Colloquium by Mark Hahn on "Performance: current and upcoming systems"This week's colloquium: "Performance: current and upcoming systems" by Mark Hahn from SHARCNET. 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. Virtual | COCO - 11 Jan 2023 |
11 Jan 12:00 pm 1:00 pmCO Colloquium by Mark Hahn on "Performance: current and upcoming systems"This week's colloquium: "Data Preparation" by Shadi Khalifa from CAC. 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 registration is required. Virtual | COCO 15 Mar 2023 |
12 Jan 11:00 am 12:00 pmScientific Computing Lecture (PHY1510)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. | PHY1610 - Winter 2023 |
12 Jan 11:00 am 12:30 pmEES1137 Lecture 2In 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. | EES1137 - Winter 2023 |
16 Jan 1:00 pm 4:00 pmIntro to the Linux Command LineWorking 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 coursse which will cover basic commands. It could be a great boon for your productivity.Format: In person, but also broadcast and recorded. SciNet Teaching Room | SCMP101 - Jan 2023 |
17 Jan 10:30 am 12:00 pmEES1137 Lecture 3In 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. | EES1137 - Winter 2023 |
17 Jan 11:00 am 12:00 pmScientific Computing Lecture (PHY1610)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. | PHY1610 - Winter 2023 |

