Physical sciences

Courses which emphasize the physical sciences
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Courses tagged with "Physical sciences"

PHY1610 Scientific Computing for Physicists (Winter 2024)

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, ...).

This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.

This is an in-person course.

Teacher: Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 9 Jan 2024
End date: 12 Apr 2024
Scientific Computing Credits: 28
High Performance Computing Credits: 8

PHY1610 Scientific Computing for Physicists (Winter 2023)

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, ...).

This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.

Teacher: Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 10 Jan 2023
End date: 30 Apr 2023
Scientific Computing Credits: 28
High Performance Computing Credits: 8

PHY1610 Scientific Computing for Physicists (Winter 2022)

This is the site for the course given in 2022.  For the Winter 2023 PHY1610 course, please go to https://scinet.courses/1234 .

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, ...).


This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.

Teacher: Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 11 Jan 2022
End date: 1 May 2022
Scientific Computing Credits: 28
High Performance Computing Credits: 8

PHY1610 Scientific Computing for Physicists (Winter 2021)

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, ...).
Teachers: Marcelo Ponce, Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 12 Jan 2021
End date: 8 Apr 2021

PHY1610 Scientific Computing for Physicists (Winter 2020)

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, ...).
Teachers: Marcelo Ponce, Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 7 Jan 2020
End date: 30 Apr 2020

PHY1610 Scientific Computing for Physicists (Winter 2019)

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, ...).
Teachers: Marcelo Ponce, Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 8 Jan 2019
End date: 4 Apr 2019

PHY1610 Scientific Computing for Physicists (Winter 2018)

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, ...).
Teachers: Alexey Fedoseev, Bruno Mundim, Marcelo Ponce, Erik Spence, Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 4 Jan 2018
End date: 3 Apr 2018

PHY1610 Scientific Computing for Physicists (Winter 2017)

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, ...).
Teachers: Scott Northrup, Marcelo Ponce, Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 5 Jan 2017
End date: 4 Apr 2017

PHY1610 Scientific Computing for Physicists (Winter 2016)

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, ...).
Teachers: Scott Northrup, Marcelo Ponce, Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 12 Jan 2016
End date: 7 Apr 2016

EES1137 Quantitative Applications for Data Analysis (Winter 2024)

In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing 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 must enrol through Acorn/ROSI.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 9 Jan 2024
End date: 4 Apr 2024
Scientific Computing Credits: 8
Data Science Credits: 28

EES1137 Quantitative Applications for Data Analysis (Winter 2023)

In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing 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 must enrol through Acorn/ROSI.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 10 Jan 2023
End date: 6 Apr 2023
Scientific Computing Credits: 8
Data Science Credits: 28

EES1137 Quantitative Applications for Data Analysis (Winter 2022)

In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing 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 enrol through Acorn/ROSI.
This course is part of the EES graduate program and will be taught online this semester.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 11 Jan 2022
End date: 7 Apr 2022
Scientific Computing Credits: 8
Data Science Credits: 28

EES1137 Quantitative Applications for Data Analysis (Winter 2021)

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.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 12 Jan 2021
End date: 1 Apr 2021

EES1137 Quantitative Applications for Data Analysis (Winter 2020)

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.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 8 Jan 2020
End date: 3 Apr 2020

EES1137 Quantitative Applications for Data Analysis (Winter 2019)

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.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 9 Jan 2019
End date: 5 Apr 2019

EES1137 Quantitative Applications for Data Analysis (Winter 2018)

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.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 5 Jan 2018
End date: 6 Apr 2018

EES1137 Quantitative Applications for Data Analysis (Winter 2017)

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.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 4 Jan 2017
End date: 31 Mar 2017