Upcoming SciNet Events

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June,2020
Tue 9th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 9th Jun
12:15 pm
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In lieu of its annual Ontario Summer School, SciNet in collaboration with CAMH will be offering weekly virtual summer training on High Performance Computing from June through to August. Topics will include parallel programming, Linux shell, large scale batch processing, biomedical computations, and performance Python and R. The schedule will be announced on the site in the near future. Location: SciNet Online
Tue 9th Jun
12:30 pm
2:00 pm
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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 weeks. 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: SciNet Online
Thu 11th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 11th Jun
12:30 pm
2:00 pm
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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 weeks. 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: SciNet Online
Fri 12th Jun
12:30 pm
2:00 pm
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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 weeks. 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: SciNet Online
Tue 16th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 16th Jun
12:30 pm
2:00 pm
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Working with HPC systems involves using the Linux command line.  This short session will cover basic Linux commands to get you initiated with the command line. Location: SciNet Online
Wed 17th Jun
12:30 pm
2:00 pm
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Working with HPC systems involves using the Linux command line.  This short session will cover basic Linux commands to get you initiated with the command line. Location: SciNet Online
Thu 18th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 18th Jun
12:30 pm
2:00 pm
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Working with HPC systems involves using the Linux command line.  This short session will cover basic Linux commands to get you initiated with the command line. Location: SciNet Online
Tue 23rd Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 23rd Jun
12:30 pm
2:00 pm
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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. Location: SciNet Online
Wed 24th Jun
12:30 pm
2:00 pm
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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. Location: SciNet Online
Thu 25th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 25th Jun
12:30 pm
2:00 pm
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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. Location: SciNet Online
July,2020
Tue 7th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 7th Jul
12:30 pm
2:00 pm
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Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.  Location: SciNet Online
Wed 8th Jul
12:30 pm
2:00 pm
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Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.  Location: SciNet Online
Thu 9th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 9th Jul
12:30 pm
2:00 pm
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Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.  Location: SciNet Online
Tue 14th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 14th Jul
12:30 pm
2:00 pm
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Introduction to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. Location: SciNet Online
Wed 15th Jul
12:30 pm
2:00 pm
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Introduction to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. Location: SciNet Online
Thu 16th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 16th Jul
12:30 pm
2:00 pm
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Introduction to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. Location: SciNet Online
Tue 21st Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 21st Jul
12:30 pm
2:00 pm
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Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. Finally, we will cover how to perform functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: SciNet Online
Wed 22nd Jul
12:30 pm
2:00 pm
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Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. Finally, we will cover how to perform functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: SciNet Online
Thu 23rd Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 23rd Jul
12:30 pm
2:00 pm
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Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. Finally, we will cover how to perform functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: SciNet Online
Tue 28th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Tue 28th Jul
12:30 pm
2:00 pm
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Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online
Wed 29th Jul
12:30 pm
2:00 pm
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Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online
Thu 30th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Online
Thu 30th Jul
12:30 pm
2:00 pm
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Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online
August,2020
Tue 4th Aug
12:30 pm
2:00 pm
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Learn how to model the neural network in the brain using Python. Location: SciNet Online
Wed 5th Aug
12:30 pm
2:00 pm
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Learn how to model the neural network in the brain using Python. Location: SciNet Online
Fri 7th Aug
12:30 pm
2:00 pm
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Learn how to model the neural network in the brain using Python. Location: SciNet Online
Tue 11th Aug
12:30 pm
2:00 pm
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Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online
Wed 12th Aug
12:30 pm
2:00 pm
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Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online
Thu 13th Aug
12:30 pm
2:00 pm
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Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online
Tue 18th Aug
12:30 pm
2:00 pm
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Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. 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: SciNet Online
Wed 19th Aug
12:30 pm
2:00 pm
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Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. 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: SciNet Online
Thu 20th Aug
12:30 pm
2:00 pm
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Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. 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: SciNet Online
Tue 25th Aug
12:30 pm
2:00 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. Location: SciNet Online
Wed 26th Aug
12:30 pm
2:00 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. Location: SciNet Online
Thu 27th Aug
12:30 pm
2:00 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. Location: SciNet Online
September,2020
Tue 8th Sep
11:00 am
12:00 pm
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This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online
Tue 8th Sep
12:00 pm
1:30 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 due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online
Wed 9th Sep
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Online
Thu 10th Sep
11:00 am
12:00 pm
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This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online
Thu 10th Sep
12:00 pm
1:30 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 due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online
Tue 15th Sep
11:00 am
12:00 pm
Add event to google
This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online
Tue 15th Sep
12:00 pm
1:30 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 due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online
Wed 16th Sep
12:00 pm
1:00 pm
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Monthly user meeting at SciNet, now virtual, with user discussion and a TechTalk. The topic on 16th Sept 2020 will be "Security Best Practices". Location: SciNet Online
Thu 17th Sep
11:00 am
12:00 pm
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This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online
Thu 17th Sep
12:00 pm
1:30 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 due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online
Mon 21st Sep
12:30 pm
2:00 pm
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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 Online
Tue 22nd Sep
11:00 am
12:00 pm
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This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online