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November,2023
28 Nov 9:00 am 10:30 am

MSC1090 lecture 23

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, 9:00-10:30am, in SS1085.
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.
SS1085
MSC1090 - Fall 2023Show in Google map
30 Nov 9:00 am 10:30 am

MSC1090 lecture 24

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, 9:00-10:30am, in SS1085.
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.
SS1085
MSC1090 - Fall 2023Show in Google map
December,2023
1 Dec 1:00 pm 4:00 pm

Intro to Linux Command Line

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 coursse which will cover basic commands. It could be a great boon for your productivity.Format: Virtual Virtual
SCMP101 - Dec 2023Show in Google map
4 Dec 12:30 pm 2:00 pm

Intro to MPI 1/3

Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.Format: Virtual Virtual
HPC123 - Dec 2023Show in Google map
6 Dec 12:30 pm 2:00 pm

Intro to MPI 2/3

Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.Format: Virtual Virtual
HPC123 - Dec 2023Show in Google map
8 Dec 12:30 pm 2:00 pm

Intro to MPI 3/3

Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.Format: Virtual Virtual
HPC123 - Dec 2023Show in Google map
13 Dec 1:00 pm 2:30 pm

Intro to Niagara

In 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: Virtual Virtual
HPC105 - Dec 2023Show in Google map
January,2024
9 Jan 10:00 am 12:00 pm

EES1137: Lecture 1

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.
UTSC: AA207
EES1137 - Winter 2024Show in Google map
9 Jan 11:00 am 12:00 pm

PHY1610 Scientific Computing Lecture

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.
PHY1610 - Winter 2024
10 Jan 1:00 pm 2:30 pm

Intro to Niagara

In 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: Virtual Virtual
HPC105 - Jan 2024Show in Google map