Upcoming SciNet Events

SciNet EventsRefresh calendars Add to google calendar
June,2016
Wed 8th Jun
12:00 pm
1:00 pm
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This meeting includes: pizza, user discussion, and a techtalk by Michael Wong on C++17. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 20th Jun
10:00 am
5:00 pm
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Tutorial session on "Scientific Visualization with VisIt" at CANHEIT-HPCS2-16 (canheit-hpcs.ualberta.ca) Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
July,2016
Mon 11th Jul
9:00 am
to Fri 15th Jul
5:00 pm
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A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Location: Wallberg Building (UofT- St. George Campus), Rm 116
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184-200 College Street, Toronto, M56 3E5, Canada
Mon 11th Jul
9:30 am
12:30 pm
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A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
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184-200 College Street, Toronto, M56 3E5, Canada
Mon 11th Jul
9:30 am
12:30 pm
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Part of the 2016 Ontario Summer School, 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, essential issues, problem characteristics as they apply to parallelism and a high level overview of parallel programming models. Strategies of running large sets of serial processes using e.g. GNU parallel, will also be presented. Location: SciNet Conference Room (old location on McCaul Street)
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256 McCaul Street, Toronto, M5T 1W5, Canada
Mon 11th Jul
1:30 pm
4:30 pm
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A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Mon 11th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Tue 12th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Tue 12th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Tue 12th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Tue 12th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Wed 13th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Wed 13th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Wed 13th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Wed 13th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Thu 14th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Thu 14th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Thu 14th Jul
1:30 pm
4:30 pm
Add event to google
Part of the 2016 Ontario Summer School, this two-day session is an introductory course covering programming and computing on GPUs - graphics processing units - which are an increasingly common presence in massively parallel computing architectures. This session will cover both of the available C-like programming frameworks: NVIDIA’s CUDA C. The basics of GPU programming will be covered, and students will work through a number of hands on examples. Demonstrations of profiling and debugging applications running on the GPU will also be included. The structuring of data and computations that makes full use of the GPU will be discussed in detail. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Thu 14th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Thu 14th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Fri 15th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Fri 15th Jul
9:30 am
12:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Fri 15th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
Fri 15th Jul
1:30 pm
4:30 pm
Add event to google
A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119
Show in Google map
184-200 College Street, Toronto, M56 3E5, Canada
August,2016
Fri 12th Aug
9:30 am
4:30 pm
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Visualization session at the Ontario HPC Summer School East, at the University of Ottawa hosted by CAC. Location: SciNet Conference Room (old location on McCaul Street)
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256 McCaul Street, Toronto, M5T 1W5, Canada
Sun 14th Aug
4:00 pm
7:00 pm
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As part of the "2016 Industrial Problem Solving Workshop" to be hosted at the Fields Institute, SciNet will provide an introduction to High Performance Computing resources, as well as support for participants who decide to tackle this problems numerical utilizing supercomputer resources. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
September,2016
Wed 14th Sep
10:00 am
11:30 am
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In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 14th Sep
12:00 pm
1:00 pm
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pizza, user discussion, and a techtalk on "Overview of 3D Scientific Visualization Tools" Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 20th Sep
11:00 am
12:00 pm
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Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 22nd Sep
11:00 am
12:00 pm
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Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 27th Sep
11:00 am
12:00 pm
Add event to google
Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 27th Sep
12:00 pm
1:30 pm
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Live broadcast of the Q&A session for the 2016 Visualization Challenge: "Visualize this!/Faites-nous voir ça!" competition Part of Visualization Challenge -- live Q&A session, Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 29th Sep
11:00 am
12:00 pm
Add event to google
Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
October,2016
Tue 4th Oct
11:00 am
12:00 pm
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Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 5th Oct
4:30 pm
6:00 pm
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Information sessions for the 2017 Big Data Challenge for High Schools Students. Part of 2016-17 Big Data Challenge for High School Students -- Information Sessions, Location: Villanova College
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2480 15th Sideroad, King City, L7B 1A4, Canada
Thu 6th Oct
11:00 am
12:00 pm
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Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 6th Oct
4:30 pm
6:00 pm
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Information sessions for the 2017 Big Data Challenge for High Schools Students. Part of 2016-17 Big Data Challenge for High School Students -- Information Sessions, Location: Earl Haig Secondary School
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100 Princess Ave, Toronto, M2N 3R7, Canada
Fri 7th Oct
4:30 pm
6:00 pm
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Information sessions for the 2017 Big Data Challenge for High Schools Students. Part of 2016-17 Big Data Challenge for High School Students -- Information Sessions, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 11th Oct
11:00 am
12:00 pm
Add event to google
Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 11th Oct
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Wed 12th Oct
10:00 am
11:30 am
Add event to google
In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 12th Oct
12:00 pm
1:00 pm
Add event to google
pizza, user discussion, and a tech talkabout SciNet's new "Knights Landing" KNL nodes Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 13th Oct
11:00 am
12:00 pm
Add event to google
Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 13th Oct
12:00 pm
1:30 pm
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This is a collection of non-training events at, by, or just announced by SciNet. Part of General Event Announcements, Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 13th Oct
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Tue 18th Oct
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
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569 Spadina Crescent, Toronto, M5S 2J7, Canada
Wed 19th Oct
10:00 am
1:00 pm
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Learn the basics of how to use the unix shell in two hours. Includes an mild introduction to bash scripting as well. Very useful for new users of SciNet that have little or no experience with unix or linux. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 20th Oct
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Tue 25th Oct
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Thu 27th Oct
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
November,2016
Tue 1st Nov
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Thu 3rd Nov
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Tue 8th Nov
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Wed 9th Nov
10:00 am
11:30 am
Add event to google
In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 10th Nov
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Tue 15th Nov
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Thu 17th Nov
12:00 pm
1:00 pm
Add event to google
The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108
Show in Google map
569 Spadina Crescent, Toronto, M5S 2J7, Canada
Tue 22nd Nov
11:00 am
12:00 pm
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Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
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140 St. George Street, Toronto, M5S 2G6, Canada
Thu 24th Nov
11:00 am
12:00 pm
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Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
Show in Google map
140 St. George Street, Toronto, M5S 2G6, Canada
Tue 29th Nov
11:00 am
12:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
Show in Google map
140 St. George Street, Toronto, M5S 2G6, Canada
December,2016
Thu 1st Dec
11:00 am
12:00 pm
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Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
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140 St. George Street, Toronto, M5S 2G6, Canada
Tue 6th Dec
11:00 am
12:00 pm
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Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
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140 St. George Street, Toronto, M5S 2G6, Canada
Thu 8th Dec
11:00 am
12:00 pm
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Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
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140 St. George Street, Toronto, M5S 2G6, Canada
Tue 13th Dec
11:00 am
12:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
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140 St. George Street, Toronto, M5S 2G6, Canada
Wed 14th Dec
10:00 am
11:30 am
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In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 14th Dec
12:00 pm
1:00 pm
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pizza, user discussion, and a techtalk by Ricardo Harripaul on "Computationally Mapping Autosomal Recessive Intellectual Disability" Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 15th Dec
11:00 am
12:00 pm
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Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205
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140 St. George Street, Toronto, M5S 2G6, Canada
January,2017
Wed 4th Jan
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. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 5th Jan
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
Fri 6th Jan
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. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Fri 6th Jan
2:00 pm
3:00 pm
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Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. Part of Undergrad Seminars on Advanced Research Computing, Data Science and Visualization, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 10th Jan
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
Wed 11th Jan
10:00 am
11:30 am
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In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 11th Jan
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. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Wed 11th Jan
12:00 pm
1:00 pm
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pizza, user discussion, and a techtalk on "Python in the browser on SciNet: JupyterHub" Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 12th Jan
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
Fri 13th Jan
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. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Fri 13th Jan
2:00 pm
3:00 pm
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Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. Part of Undergrad Seminars on Advanced Research Computing, Data Science and Visualization, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 17th Jan
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
Wed 18th Jan
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. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 19th Jan
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
Fri 20th Jan
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. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Fri 20th Jan
2:00 pm
3:00 pm
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Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. Part of Undergrad Seminars on Advanced Research Computing, Data Science and Visualization, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 24th Jan
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
Wed 25th Jan
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. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 26th Jan
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
Fri 27th Jan
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. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Fri 27th Jan
2:00 pm
3:00 pm
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Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. Part of Undergrad Seminars on Advanced Research Computing, Data Science and Visualization, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 31st Jan
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
February,2017
Wed 1st Feb
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. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 2nd Feb
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
Fri 3rd Feb
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. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Fri 3rd Feb
2:00 pm
3:00 pm
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Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. Part of Undergrad Seminars on Advanced Research Computing, Data Science and Visualization, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 7th Feb
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
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th Feb
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. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 9th Feb
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
Fri 10th Feb
11:00 am
12:00 pm
Add event to google
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. Location: MW 160 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Fri 10th Feb
2:00 pm
3:00 pm
Add event to google
Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. Part of Undergrad Seminars on Advanced Research Computing, Data Science and Visualization, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 13th Feb
2:00 pm
5:00 pm
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Learn the basics of how to use the unix shell in two hours. Includes an mild introduction to bash scripting as well. Very useful for new users of SciNet that have little or no experience with unix or linux. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 14th Feb
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
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada