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

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)

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)

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

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

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

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)

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

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

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

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)

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

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

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

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)

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)

A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140

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 Teaching Room MaRS 1140

Increase you Linux (bash) command line productivity. Requires some basic Linux command line experience. Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

Monthly user meeting at SciNet with pizza, a techtalk (TBA) and user discussion. Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140

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 Teaching Room MaRS 1140