Upcoming SciNet Events

SciNet EventsRefresh calendars Add to google calendar
August,2019
Mon 19th Aug
11:00 am
6:00 pm
Add event to google
The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 20th Aug
11:00 am
6:00 pm
Add event to google
The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 21st Aug
11:00 am
6:00 pm
Add event to google
The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 22nd Aug
11:00 am
6:00 pm
Add event to google
The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 23rd Aug
11:00 am
6:00 pm
Add event to google
The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
September,2019
Mon 9th Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 11th Sep
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 12th Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 16th Sep
1:00 pm
4:00 pm
Add event to google
Working with many of the HPC systems in Ontario involves using the Linux 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 basic commands in the Linux environment. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 16th Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 19th Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 23rd Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 26th Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 27th Sep
12:00 pm
1:00 pm
Add event to google
Meetings for visualization enthusiasts to discuss and share ideas about visualization and novel data representations. Part of UofT Viz Discussion Group, Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 30th Sep
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
October,2019
Tue 1st Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 3rd Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 3rd Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 4th Oct
1:00 pm
4:00 pm
Add event to google
Introductory workshop to get started in the usage of version control GIT.This workshop is held in collaboration with UofT-Libraries. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 7th Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 10th Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 10th Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 11th Oct
1:00 pm
4:00 pm
Add event to google
Learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing. -- Prerequisites: C, C++ or Fortran programming, experience editing and compiling code in a Linux environment. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 15th Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 16th Oct
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 16th Oct
12:00 pm
1:00 pm
Add event to google
Monthly user meeting at SciNet with pizza, techtalks and user discussion. This month, we will focus on two topics: 1. Using the newer 2019b Niagara software stack. 2. The Compute Canada Resource Allocation Competition. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 17th Oct
1:00 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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 17th Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 21st Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 22nd Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 23rd Oct
1:00 pm
4:00 pm
Add event to google
Increase you Linux (bash) command line productivity. Requires some basic Linux command line experience. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 24th Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 24th Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 28th Oct
1:00 pm
4:00 pm
Add event to google
Learn the basics of Message Passing Interface (MPI) programming. Prerequisites: C/C++ or Fortran programming. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 28th Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 29th Oct
1:00 pm
2:00 pm
Add event to google
New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 31st Oct
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
November,2019
Fri 1st Nov
12:00 pm
1:00 pm
Add event to google
Meetings for visualization enthusiasts to discuss and share ideas about visualization and novel data representations. Part of UofT Viz Discussion Group, Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 4th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 5th Nov
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 7th Nov
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 7th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 11th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 12th Nov
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 13th Nov
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 13th Nov
12:00 pm
1:00 pm
Add event to google
Monthly user meeting at SciNet with pizza, a techtalk on the coming Niagara expansion and user discussion. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 14th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 14th Nov
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Mon 18th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 21st Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 25th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 25th Nov
1:00 pm
4:00 pm
Add event to google
The goal is for students, new to GPGPU but familiar with programming in C/C++, to leave being able to write simple kernels for their own problems, and understand the tools and techniques needed to improve the results. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 26th Nov
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 27th Nov
1:00 pm
4:00 pm
Add event to google
Working with many of the HPC systems in Ontario involves using the Linux 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 basic commands in the Linux environment. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 28th Nov
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 28th Nov
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 29th Nov
12:00 pm
1:00 pm
Add event to google
Meetings for visualization enthusiasts to discuss and share ideas about visualization and novel data representations. Part of UofT Viz Discussion Group, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
December,2019
Mon 2nd Dec
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 3rd Dec
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 5th Dec
1:00 pm
2:00 pm
Add event to google
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 5th Dec
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 with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 11th Dec
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
January,2020
Tue 7th Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th Jan
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th Jan
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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 9th Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 10th Jan
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
Tue 14th Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 15th Jan
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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Wed 15th Jan
1:00 pm
4:00 pm
Add event to google
Working with many of the HPC systems in Ontario involves using the Linux 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 basic commands in the Linux environment. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 16th Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 17th Jan
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
Tue 21st Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 22nd Jan
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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 23rd Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 24th Jan
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
Tue 28th Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 29th Jan
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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 30th Jan
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 31st Jan
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
February,2020
Mon 3rd Feb
1:00 pm
4:00 pm
Add event to google
Apply MPI to realistic scientific computing examples and learn to use advanced MPI techniques such as non-blocking communications. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 4th Feb
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 5th 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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 6th Feb
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 7th 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
Tue 11th Feb
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 12th Feb
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 12th 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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Wed 12th Feb
12:00 pm
1:00 pm
Add event to google
Monthly user meeting at SciNet with pizza, user discussion, and a techtalk. This month: Introducing the Mist GPU Cluster and user discussion. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 13th Feb
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 14th 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
Wed 19th Feb
1:00 pm
4:00 pm
Add event to google
Learn how to write bash scripts, use environment variables, how to control process, and much more. Requires some linux basic command line experience. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 25th Feb
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 26th 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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 27th Feb
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 28th 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
March,2020
Tue 3rd Mar
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 4th Mar
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 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 5th Mar
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 6th Mar
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