SciNet Events |
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| February,2021 | |
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Tue 23rd Feb 10:30 am 12:00 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Tue 23rd Feb 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 25th Feb 11:00 am 12:30 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Thu 25th Feb 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
| March,2021 | |
Mon 1st Mar 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 2nd Mar 10:30 am 12:00 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Tue 2nd Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 4th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 4th Mar 11:00 am 12:30 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Mon 8th Mar 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 9th Mar 10:30 am 12:00 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Tue 9th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Wed 10th Mar 10:00 am 11:30 am | A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Online |
Wed 10th Mar 12:00 pm 1:00 pm | Monthly (virtual) user meeting at SciNet with user discussion and a TechTalk. The topic of 10 March 2021 is the "SciNet at Scale" event. Location: SciNet Online |
Thu 11th Mar 11:00 am 12:30 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Thu 11th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Mon 15th Mar 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 16th Mar 10:30 am 12:00 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Tue 16th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 18th Mar 11:00 am 12:30 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Thu 18th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Mon 22nd Mar 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 23rd Mar 10:30 am 12:00 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Tue 23rd Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 25th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 25th Mar 11:00 am 12:30 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Mon 29th Mar 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 30th Mar 10:30 am 12:00 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Tue 30th Mar 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
| April,2021 | |
Thu 1st Apr 11:00 am 12:30 pm | In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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 have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Part of Quantitative Applications for Data Analysis, Location: SciNet Online |
Thu 1st Apr 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Mon 5th Apr 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 6th Apr 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Thu 8th Apr 11:00 am 12:00 pm | 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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online |
Mon 12th Apr 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Wed 14th Apr 10:00 am 11:30 am | A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Online |
Wed 14th Apr 12:00 pm 1:00 pm | Monthly (virtual) user meeting at SciNet with user discussion and a TechTalk. The topic on 14 April 2021 will be the new "Rouge" cluster at SciNet, donated as part of the AMD COVID-19 HPC Fund. Location: SciNet Online |
Mon 19th Apr 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Mon 26th Apr 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 27th Apr 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Thu 29th Apr 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
| May,2021 | |
Mon 3rd May 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 4th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Thu 6th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Mon 10th May 1:00 pm 2:00 pm | This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of Introduction to Programming with R, Location: SciNet Online |
Tue 11th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Wed 12th May 10:00 am 11:30 am | A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Online |
Thu 13th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Tue 18th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Thu 20th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Tue 25th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Thu 27th May 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
| June,2021 | |
Tue 1st Jun 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
Thu 3rd Jun 11:00 am 12:00 pm | This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online |
| July,2021 | |
Sun 11th Jul 1:00 pm to Fri 23rd Jul 6:00 pm | This summer school will familiarize the best students in computational sciences with major state-of-the-art aspects of HPC for a variety of scientific disciplines, catalyze the formation of networks, provide advanced mentoring, facilitate international exchange and open up further career options. Leading Canadian, European, Japanese and American computational scientists and HPC technologists will offer instruction in parallel sessions on a variety of topics as: HPC challenges in major scientific disciplines, HPC programming proficiencies, Performance analysis and profiling, Software engineering, Numerical libraries, Big data analysis and analytics, Machine learning, Scientific visualization, and Canadian, European, Japanese and US HPC infrastructure. This events was originally planned to take place in July 2020 but has been moved to July 2021 instead.  Unspecified Location |
