Upcoming SciNet Events

SciNet EventsRefresh calendars Add to google calendar
June,2018
Wed 6th Jun
12:00 pm
3:00 pm
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Principles and uses of relational databases with practical examples using python and sqlite. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 11th Jun
9:00 am
to Fri 15th Jun
5:00 pm
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The Compute Ontario Summer School on Scientific and High Performance Computing is an annual educational event for graduate/undergraduate students, postdocs and researchers who are engaged in a compute intensive research. Held geographically in the west, centre and east of the province of Ontario, the summer school provides attendees with the opportunity to learn and share knowledge and experience in high performance and technical computing on modern HPC platforms. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 11th Jun
9:30 am
12:30 pm
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This half-day session will provide an introduction to basic concepts of high-performance computing. It is intended to be a high level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism, and a high level overview of parallel programming models. Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Mon 11th Jun
1:30 pm
4:30 pm
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Working with many of the HPC systems in Ontario involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course. This hands on session will cover basic commands and scripting, as well as touching on some powerful constructs like awk. It could be a great boon for your productivity! Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 11th Jun
1:30 pm
4:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Mon 11th Jun
1:30 pm
4:30 pm
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This 1 day course will introduce python packages and approaches for medical imaging applications (MRI specifically). We will give an overview of specific command line based tools (freesurfer/FSL) for image analysis introduce how to interface with them using python. Specific python packages will be for nibabel (for MR image i/o), nilearn (for plotting/visualization) and nipype (for pipeline development/parallelization). Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
9:30 am
12:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
9:30 am
12:30 pm
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This half-day session offers a brief introduction to R, with a focus on data analysis and statistics. We will discuss and introduce the following topics: the R interface, primitive data types, lists, vectors, matrices, and data frames - a crucial data type in data analysis and a trademark in the R language. Advanced topics to be covered include: basics statistics and function creation; *apply family functions; and basics of scripting. Time depending we may cover and discuss some data management strategies (ie. saving results, workspaces and installing packages) and basic plotting. Part of Introduction to R, Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
9:30 am
12:30 pm
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Approaches to parallelization (utilizing SciNet systems) for image analysis. Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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Machine-learning (ML) is becoming increasingly popular tool for analyzing large datasets and performing predictive tasks in various fields. In neuroimaging domain, several studies have applied ML approaches towards clustering, classification, regression problems. This course will introduce some of these applications based on publicly available MR imaging data (ABIDE). It will provide an overview of few ML models (supervised and unsupervised), and go over typical pitfalls as well as robust validation paradigms for performance evaluation. Part of Cancelled session: CO Summer School Central: Machine Learning for Neuroimaging, Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. Location: Wilson Hall - New College, WI 1017
Show in Google map
40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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This 1/2 day course will focus of cortical surface based neuroimaging analysis. We will talk use SciNet to run freesurfer's recon-all pipeline to define the cortical surfaces in our datasets. We will then use Connectome-Workbench (tools from the Human Connectome Project, or HCP) to analyse and visualize our data. Part of HCP with HPC: Surface Based Neuroimaging Analysis, Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Tue 12th Jun
1:30 pm
4:30 pm
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In this half-day session, you will be taught how to use python for research computing purposes. Topics include: the basics of python, automation, numpy, scipy, file i/o, and visualization. Part of Introduction to Python, Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
9:30 am
12:30 pm
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This 3 hours course will focus of data cleaning and statistical analysis of large scale genetic data using PLINK and R softwares. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results. Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
9:30 am
12:30 pm
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This half-day session will cover parallel programming in python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
9:30 am
12:30 pm
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This is an introductory course covering programming and computing on GPUs --- graphics processing units --- which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
1:30 pm
4:30 pm
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Next generation sequencing is revolutionizing the way molecular sciences and biology is performed while providing a population-level understanding of genetic variation in organisms. In this course, you will be introduced to data types in next generation sequencing, analysis methods and best practices to go from raw sequencing data to fully reconstructed genomes. Attendees will also be exposed to variant calling methods to assess genetic variation and the use of parallel methods to scale large analysis on a high-performance computing cluster. Principles in this course can be applied to the other workshops in this stream for genome-wide association analysis and RNA sequencing analysis. Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
1:30 pm
4:30 pm
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This is an introductory course covering programming and computing on GPUs --- graphics processing units --- which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wilson Hall - New College, WI 1017
Show in Google map
40 Willcocks Street, Toronto, M5S 1C6, Canada
Wed 13th Jun
1:30 pm
4:30 pm
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This half days session offers an overview of machine learning tools available in Python. Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Thu 14th Jun
9:30 am
12:30 pm
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The class will cover basic pipeline of pre-alignment QC of FASATQ files, read alignments to the reference genome, Post alignment visualization using IGV, and differential expression analysis using R. If time permits, enrichment analysis using GSEA will also be covered. Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Thu 14th Jun
9:30 am
12:30 pm
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This half-day session will introduce neural network programming concepts, theory and techniques in Python. Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Thu 14th Jun
9:30 am
12:30 pm
Add event to google
This is an introductory course covering programming and computing on GPUs --- graphics processing units --- which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wilson Hall - New College, WI 1017
Show in Google map
40 Willcocks Street, Toronto, M5S 1C6, Canada
Thu 14th Jun
1:30 pm
4:30 pm
Add event to google
This is an introductory course covering programming and computing on GPUs --- graphics processing units --- which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wilson Hall - New College, WI 1017
Show in Google map
40 Willcocks Street, Toronto, M5S 1C6, Canada
Thu 14th Jun
1:30 pm
4:30 pm
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A mix of lecture and hands-on to introduce specialized scientific visualization software such as ParaView and VisIt. Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Thu 14th Jun
1:30 pm
4:30 pm
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This course will teach about analyzing MRI data with R. We will focus on volumes/cortical thickness, etc., and teach both classic massively univariate as well as (to us!) more interesting hierarchical bayesian approaches. Location: Wilson Hall - New College, WI 524
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Fri 15th Jun
9:30 am
12:30 pm
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Debugging and profiling are important steps in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code with gdb and the debugging of parallel (MPI and threaded) codes. If you bring your own code, and can explain your problem or concerns, we'll also give you advice and work with you to try to improve it. Part of Debugging, Profiling and BYOC, Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Fri 15th Jun
9:30 am
12:30 pm
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How to find and use public datasets for neuroscience research with a focus on transcriptomics and neuroimaging. Introduction and guides to some of the largest datasets will be provided (Allen human brain atlases, BrainEAC, ADNI, and the Human Connectome Project). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 15th Jun
9:30 am
12:30 pm
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In this one-day session, lectures and hands-on labs are interspersed, and the students will learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP's execution and memory model, performance, reductions and load balancing. Part of Shared Memory Programming with OpenMP, Location: Wilson Hall - New College, WI 1017
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Fri 15th Jun
1:30 pm
4:30 pm
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Debugging and profiling are important steps in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code with gdb and the debugging of parallel (MPI and threaded) codes. If you bring your own code, and can explain your problem or concerns, we'll also give you advice and work with you to try to improve it. Part of Debugging, Profiling and BYOC, Location: Wilson Hall - New College, WI 2006
Show in Google map
40 Willcocks Street, Toronto, M5S 1C6, Canada
Fri 15th Jun
1:30 pm
4:30 pm
Add event to google
In this one-day session, lectures and hands-on labs are interspersed, and the students will learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP's execution and memory model, performance, reductions and load balancing. Part of Shared Memory Programming with OpenMP, Location: Wilson Hall - New College, WI 1017
Show in Google map
40 Willcocks Street, Toronto, M5S 1C6, Canada
Fri 15th Jun
1:30 pm
4:30 pm
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An afternoon of biomedical hacking. Bring your own code! Location: Wilson Hall - New College, WI 1016
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40 Willcocks Street, Toronto, M5S 1C6, Canada
Mon 25th Jun
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Part of CDL Machine Learning Bootcamp, Location: Rotman School of Management - LL1035
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105 St. George St., Toronto, M5S 3E6, Canada
Wed 27th Jun
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Part of CDL Machine Learning Bootcamp, Location: Rotman School of Management - LL1020
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105 St. George St., Toronto, M5S 3E6, Canada
Fri 29th Jun
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Part of CDL Machine Learning Bootcamp, Location: Banting Building - OnRamp RBC Innovation Hub
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100 College Street, Toronto, M5G 1L5, Canada
July,2018
Tue 3rd Jul
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Part of CDL Machine Learning Bootcamp, Location: Rotman School of Management - LL1060
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105 St. George St., Toronto, M5S 3E6, Canada
Thu 5th Jul
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Part of CDL Machine Learning Bootcamp, Location: Rotman School of Management - LL1060
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105 St. George St., Toronto, M5S 3E6, Canada
September,2018
Tue 11th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Science Building, MSB4279
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 12th Sep
10:00 am
12:00 pm
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A quick introduction how to use the new supercomputer Niagara. Part of Intro to SciNet/Niagara, Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 12th Sep
12:00 pm
1:30 pm
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George Stein (Dept. of Astronomy-UofT, CITA) "Machine learning cosmic structure formation" + pizzaAbstract:In modern astrophysics and cosmology, accurate simulations of the large scale structure of the universe are necessary. Usually, this is accomplished by so called N-body simulations, which calculate the full gravitational collapse of a region of the universe over its 14 billion year history. Instead of calculating this costly gravitational evolution, we trained a three-dimensional deep Convolutional Neural Network (CNN) to identify dark matter proto-haloes directly from the cosmological initial conditions, and showed that a CNN of this type can be a viable alternative in some cases. In this talk I will discuss current cosmological simulations and the invasion of machine learning techniques, with a focus on our work.For more information see https://arxiv.org/abs/1805.04537Please register for pizza purposes! Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 13th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Science Building, MSB4279
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1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 18th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Science Building, MSB4279
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 19th Sep
1:00 pm
4:00 pm
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Working with many of the HPC systems in Ontario involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course. This hands on session will cover basic commands and scripting. It could be a great boon for your productivity! Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 20th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 25th 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 Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 27th 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 Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
October,2018
Tue 2nd Oct
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 2nd Oct
1:00 pm
2:00 pm
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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. Part of Introduction to Programming with Python, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 4th Oct
12:00 pm
1:30 pm
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This is the English language Q&A session for Compute Canada's competition for the compute and storage resource allocations for 2019 (RAC). Part of Compute Canada Resource Allocation Competition for 2019 Info Session, Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 4th Oct
1:00 pm
2:00 pm
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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. Part of Introduction to Programming with Python, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 4th 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 Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 9th 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 Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 9th Oct
1:00 pm
2:00 pm
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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. Part of Introduction to Programming with Python, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 10th Oct
10:00 am
12:00 pm
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A quick introduction how to use the new supercomputer Niagara. Part of Intro to SciNet/Niagara, Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 10th Oct
12:00 pm
1:00 pm
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Pizza and discussion about the Resource Allocation Competition Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 11th Oct
1:00 pm
2:00 pm
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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. Part of Introduction to Programming with Python, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 11th 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 Science Building, MSB4279
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 16th Oct
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Science Building, MSB4279
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1 King's College Circle, Toronto, M5S 1A8, Canada
Tue 16th Oct
1:00 pm
2:00 pm
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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. Part of Introduction to Programming with Python, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 17th Oct
1:00 pm
4:00 pm
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Learn how to write bash script, use environment variables, how to control process, and much more. Requires some linux basic command line experience. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 18th Oct
1:00 pm
2:00 pm
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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. Part of Introduction to Programming with Python, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada