SciNet Events |
  |
| January,2021 | |
|---|---|
Fri 15th Jan 12:00 pm 1:00 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 |
Tue 19th Jan 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 19th Jan 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 20th Jan 10:00 am 1:00 pm | Working with many of the HPC systems in Ontario 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. This hands on session will cover basic commands. It could be a great boon for your productivity. Location: SciNet Teaching Room MaRS 1140  661 University Ave., Toronto, M5G 1M1, Canada |
Wed 20th Jan 2:00 pm 3:00 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 21st Jan 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 21st Jan 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 |
Fri 22nd Jan 12:00 pm 1:00 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 |
Tue 26th Jan 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 26th Jan 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 27th Jan 2:00 pm 3:00 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 28th Jan 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 28th Jan 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 |
Fri 29th Jan 12:00 pm 1:00 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 |
| February,2021 | |
Tue 2nd 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 2nd 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 |
Wed 3rd Feb 2:00 pm 3:00 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 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 4th 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 |
Fri 5th Feb 12:00 pm 1:00 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 |
Tue 9th 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 9th 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 |
Wed 10th Feb 10:00 am 11:30 am | A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Teaching Room MaRS 1140 661 University Ave., Toronto, M5G 1M1, Canada |
Wed 10th Feb 12:00 pm 1:00 pm | Monthly user meeting at SciNet, now virtual, with user discussion and a TechTalk. The topic on 10 Feb 2021 is TBA. Location: SciNet Online |
Wed 10th Feb 2:00 pm 3:00 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 11th 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 11th 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 |
Fri 12th Feb 12:00 pm 1:00 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 Feb 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 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 12:30 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 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 |
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 Teaching Room MaRS 1140 661 University Ave., Toronto, M5G 1M1, Canada |
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 12:00 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 |
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 12:30 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 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 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 |
