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January,2023
10 Jan 10:30 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.
EES1137 - Winter 2023
10 Jan 11:00 am 12:00 pm

Scientific 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 am

Intro to SciNet, Niagara and Mist

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: In-person as well as broadcast and recorded. SciNet Teaching Room
HPC105 - Jan 2023Show in Google map
11 Jan 12:00 pm 1:00 pm

CO 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 2023Show in Google map
12 Jan 11:00 am 12:00 pm

Scientific 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 pm

EES1137 Lecture 2

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.
EES1137 - Winter 2023
16 Jan 1:00 pm 4:00 pm

Intro to the 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: In person, but also broadcast and recorded. SciNet Teaching Room
SCMP101 - Jan 2023Show in Google map
17 Jan 10:30 am 12:00 pm

EES1137 Lecture 3

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.
EES1137 - Winter 2023
17 Jan 11:00 am 12:00 pm

Scientific 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
18 Jan 12:00 pm 1:00 pm

CO Colloquium by Erik Spence on "A comparison of neural network frameworks"

This week's colloquium: "A comparison of neural network frameworks" by Erik Spence 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 registration is required. Most presentations are recorded and uploaded to the hosting consortium video channel. Virtual
COCO - 18 Jan 2023Show in Google map
19 Jan 11:00 am 12:00 pm

Scientific 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
19 Jan 11:00 am 12:30 pm

EES1137 Lecture 4

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.
EES1137 - Winter 2023
23 Jan 12:30 pm 2:00 pm

Python to C++ #1

C++ is a high level programming language that is extremely useful for scientific applications. The language has historically had a bad reputation, but modern C++ is much improved so that your code can be relatively short and elegant. In this workshop we will teach the basics of C++ for people who are familiar with the basics of programming, and we will especially compare and contrast C++ with Python (only the material covered in SCMP142 "Intro to Programming with Python" is required). Knowing multiple programming languages may be a useful skill: while Python is a wonderful programming language, execution speed is often a practical issue for pure Python applications. For applications where this is an issue, coding in C++ can significantly improve performance. As C++ can relatively easily be integrated in a Python project, it is also possible (and common) to code just the bottleneck in that language.Format: In-person, but also will be broadcast and recorded. SciNet Training Room
SCMP241 - Jan 2023Show in Google map
24 Jan 10:30 am 12:00 pm

EES1137 Lecture 5

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.
EES1137 - Winter 2023
24 Jan 11:00 am 12:00 pm

Scientific 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
25 Jan 12:00 pm 1:00 pm

CO Colloquium by Jeff Moon on "How Research Data Management (RDM) Intersects with ARC and Why Should I Care?"

This week's colloquium: "How Research Data Management (RDM) Intersects with ARC and Why Should I Care?" by Jeff Moon 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 registration is required. Most presentations are recorded and uploaded to the hosting consortium video channel. Virtual
COCO - 25 Jan 2023Show in Google map
25 Jan 12:30 pm 2:00 pm

Python to C++ #2

C++ is a high level programming language that is extremely useful for scientific applications. The language has historically had a bad reputation, but modern C++ is much improved so that your code can be relatively short and elegant. In this workshop we will teach the basics of C++ for people who are familiar with the basics of programming, and we will especially compare and contrast C++ with Python (only the material covered in SCMP142 "Intro to Programming with Python" is required). Knowing multiple programming languages may be a useful skill: while Python is a wonderful programming language, execution speed is often a practical issue for pure Python applications. For applications where this is an issue, coding in C++ can significantly improve performance. As C++ can relatively easily be integrated in a Python project, it is also possible (and common) to code just the bottleneck in that language.Format: In-person, but also will be broadcast and recorded. SciNet Teaching Room
SCMP241 - Jan 2023Show in Google map
26 Jan 11:00 am 12:30 pm

EES1137 Lecture 6

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.
EES1137 - Winter 2023
26 Jan 11:00 am 12:00 pm

Scientific 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
27 Jan 12:30 pm 2:00 pm

Python to C++ #3

C++ is a high level programming language that is extremely useful for scientific applications. The language has historically had a bad reputation, but modern C++ is much improved so that your code can be relatively short and elegant. In this workshop we will teach the basics of C++ for people who are familiar with the basics of programming, and we will especially compare and contrast C++ with Python (only the material covered in SCMP142 "Intro to Programming with Python" is required). Knowing multiple programming languages may be a useful skill: while Python is a wonderful programming language, execution speed is often a practical issue for pure Python applications. For applications where this is an issue, coding in C++ can significantly improve performance. As C++ can relatively easily be integrated in a Python project, it is also possible (and common) to code just the bottleneck in that language.Format: In-person, but also will be broadcast and recorded. SciNet Teaching Room
SCMP241 - Jan 2023Show in Google map
31 Jan 10:30 am 12:00 pm

EES1137 Lecture 7

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.
EES1137 - Winter 2023
31 Jan 11:00 am 12:00 pm

Scientific 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
February,2023
1 Feb 12:00 pm 1:00 pm

CO Colloquium by Ching-Hsing Yu on "File Management - Packing Small Files"

This week's colloquium: "File Management - Packing Small Files" by Ching-Hsing Yu 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 registration is required.  Virtual
COCO - 1 Feb 2023Show in Google map
2 Feb 11:00 am 12:30 pm

EES1137 Lecture 8

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.
EES1137 - Winter 2023
2 Feb 11:00 am 12:00 pm

Scientific 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
7 Feb 10:30 am 12:00 pm

EES1137 Lecture 9

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.
EES1137 - Winter 2023
7 Feb 11:00 am 12:00 pm

Scientific 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
8 Feb 9:00 am 10:00 am

BCH2202 - Lecture 1

In this course students will be instructed in how to program in R. Ultimately students will learn how to use R to analyze, process and visualize data. This course is designed for students with little to no experience in programming. 
This is a graduate course that can be taken for credit by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI.
SciNet Teaching Room
BCH2202 - Winter 2023Show in Google map
8 Feb 10:00 am 11:30 am

Intro to SciNet, Niagara and Mist

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: In person, as well as broadcast and recorded. SciNet Teaching Room
HPC105 - Feb 2023Show in Google map
9 Feb 11:00 am 12:30 pm

EES1137 Lecture 10

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.
EES1137 - Winter 2023