Upcoming SciNet Events

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March,2018
Thu 1st Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 1st Mar
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 2nd Mar
11:00 am
12:00 pm
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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: MW 130 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 6th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 6th Mar
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 7th Mar
11:00 am
12:00 pm
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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: HW 308 (UTSC)
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Humanities Wing @ UTSC, Scarborough, 1265 Military Trail - Sca,
Thu 8th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 8th Mar
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 9th Mar
11:00 am
12:00 pm
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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: MW 130 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 13th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 13th Mar
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 14th Mar
11:00 am
12:00 pm
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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: HW 308 (UTSC)
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Humanities Wing @ UTSC, Scarborough, 1265 Military Trail - Sca,
Thu 15th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 15th Mar
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Galbraith Building, Rm 220
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35 St. George Street, Toronto, , Canada
Fri 16th Mar
11:00 am
12:00 pm
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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: MW 130 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 20th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 20th Mar
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 21st Mar
11:00 am
12:00 pm
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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: HW 308 (UTSC)
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Humanities Wing @ UTSC, Scarborough, 1265 Military Trail - Sca,
Thu 22nd Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 22nd Mar
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 23rd Mar
11:00 am
12:00 pm
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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: MW 130 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 27th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 27th Mar
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 28th Mar
11:00 am
12:00 pm
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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: HW 308 (UTSC)
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Humanities Wing @ UTSC, Scarborough, 1265 Military Trail - Sca,
Thu 29th Mar
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 29th Mar
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
April,2018
Tue 3rd Apr
11:00 am
12:00 pm
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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, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 3rd Apr
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 4th Apr
11:00 am
12:00 pm
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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: MW 130 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 5th Apr
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 6th Apr
11:00 am
12:00 pm
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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: HW 308 (UTSC)
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Humanities Wing @ UTSC, Scarborough, 1265 Military Trail - Sca,
Wed 11th Apr
12:00 pm
1:00 pm
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At this user group meeting, SciNet will present a quick introduction how to use the new supercomputer Niagara. The event will be live as well as broadcast. Please register if attending live, for pizza planning purposes. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 17th Apr
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 19th Apr
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 24th Apr
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 26th Apr
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
May,2018
Tue 1st May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 3rd May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 4th May
10:00 am
1:00 pm
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This workshop is part of the 2018 Chemical BioPhysics Symposium.Nowadays, researchers working in fields ranging from astrophysics to molecular dynamics, genetics, and bioninformatics, need to use Linux to beable to handle their computational needs.Linux is one of the most advanced, powerful and extremely efficientoperating systems.Learn the basics of how to use the Linux shell in a couple of hours. Veryuseful for users with no experience in the Linux command line shell.No previous experience is required.Learn to navigate the file system of a computer and manipulate filesand commands using one of the most powerful operating systems by usingsimple but extremely powerful commands in a Linux terminal. Part of Introduction to the Linux Shell, Location: Earth Sciences Centre -- ESB149
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5 Bancroft Ave., Toronto, , Canada
Tue 8th May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 10th May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 15th May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 22nd May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 23rd May
10:00 am
12:00 pm
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A quick introduction how to use the new supercomputer Niagara. Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 24th May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 29th May
11:00 am
12:00 pm
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This six-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 2.7; 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. Location: Medical Sciences Building, MS2173
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1 King's College Circle, Toronto, M5S 1A8, Canada
June,2018
Wed 6th Jun
12:00 pm
3:00 pm
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Principles and uses of relational databases with practical examples using python and sqlite. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 11th Jun
9:00 am
to Fri 15th Jun
5:00 pm
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The Compute Ontario Summer School on Scientific and High Performance Computing is an annual educational event for graduate/undergraduate students, postdocs and researchers who are engaged in a compute intensive research. Held geographically in the west, centre and east of the province of Ontario, the summer school provides attendees with the opportunity to learn and share knowledge and experience in high performance and technical computing on modern HPC platforms. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 11th Jun
9:30 am
12:30 pm
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This half-day session will provide an introduction to basic concepts of high-performance computing. It is intended to be a high level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism, and a high level overview of parallel programming models. Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Mon 11th Jun
1:30 pm
4:30 pm
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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 and scripting, as well as touching on some powerful constructs like awk. It could be a great boon for your productivity! Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 11th Jun
1:30 pm
4:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Mon 11th Jun
1:30 pm
4:30 pm
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This 1 day course will introduce python packages and approaches for medical imaging applications (MRI specifically). We will give an overview of specific command line based tools (freesurfer/FSL) for image analysis introduce how to interface with them using python. Specific python packages will be for nibabel (for MR image i/o), nilearn (for plotting/visualization) and nipype (for pipeline development/parallelization). Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
9:30 am
12:30 pm
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Approaches to parallelization (utilizing SciNet systems) for image analysis. Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
9:30 am
12:30 pm
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This half-day session offers a brief introduction to R, with a focus on data analysis and statistics. We will discuss and introduce the following topics: the R interface, primitive data types, lists, vectors, matrices, and data frames - a crucial data type in data analysis and a trademark in the R language. Advanced topics to be covered include: basics statistics and function creation; *apply family functions; and basics of scripting. Time depending we may cover and discuss some data management strategies (ie. saving results, workspaces and installing packages) and basic plotting. Part of Introduction to R, Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
9:30 am
12:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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In this half-day session, you will be taught how to use python for research computing purposes. Topics include: the basics of python, automation, numpy, scipy, file i/o, and visualization. Part of Introduction to Python, Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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Machine-learning (ML) is becoming increasingly popular tool for analyzing large datasets and performing predictive tasks in various fields. In neuroimaging domain, several studies have applied ML approaches towards clustering, classification, regression problems. This course will introduce some of these applications based on publicly available MR imaging data (ABIDE). It will provide an overview of few ML models (supervised and unsupervised), and go over typical pitfalls as well as robust validation paradigms for performance evaluation. Part of Cancelled session: CO Summer School Central: Machine Learning for Neuroimaging, Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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This 1/2 day course will focus of cortical surface based neuroimaging analysis. We will talk use SciNet to run freesurfer's recon-all pipeline to define the cortical surfaces in our datasets. We will then use Connectome-Workbench (tools from the Human Connectome Project, or HCP) to analyse and visualize our data. Part of HCP with HPC: Surface Based Neuroimaging Analysis, Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
9:30 am
12:30 pm
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This is an introductory course covering programming and computing on GPUs --- graphics processing units --- which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada