Physical sciences

Cours mettant l'accent sur les sciences physiques
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Cours signalées avec « Physical sciences »

PHY1610 Scientific Computing for Physicists (Winter 2025)

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.

Enseignant: Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 7 janv. 2025
Date de fin: : 11 avril 2025
Nombre de crédits - calcul scientifique: 28
Nombre de crédits - calcul haute performance: 8

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.

Enseignant: Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 9 janv. 2024
Date de fin: : 12 avril 2024
Nombre de crédits - calcul scientifique: 28
Nombre de crédits - calcul haute performance: 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.

Enseignant: Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 10 janv. 2023
Date de fin: : 30 avril 2023
Nombre de crédits - calcul scientifique: 28
Nombre de crédits - calcul haute performance: 8

PHY1610 Scientific Computing for Physicists (Winter 2022)

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.

Enseignant: Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 11 janv. 2022
Date de fin: : 1 mai 2022
Nombre de crédits - calcul scientifique: 28
Nombre de crédits - calcul haute performance: 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, ...).
Enseignants: Marcelo Ponce, Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 12 janv. 2021
Date de fin: : 8 avril 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, ...).
Enseignants: Marcelo Ponce, Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 7 janv. 2020
Date de fin: : 30 avril 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, ...).
Enseignants: Marcelo Ponce, Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 8 janv. 2019
Date de fin: : 4 avril 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, ...).
Enseignants: Alexey Fedoseev, Bruno Mundim, Marcelo Ponce, Erik Spence, Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 4 janv. 2018
Date de fin: : 3 avril 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, ...).
Enseignants: Scott Northrup, Marcelo Ponce, Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 5 janv. 2017
Date de fin: : 4 avril 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, ...).
Enseignants: Scott Northrup, Marcelo Ponce, Ramses van Zon
Catégorie: Scientific Computing (University Credit)
Date de début: : 12 janv. 2016
Date de fin: : 7 avril 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.

Enseignants: Alexey Fedoseev, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 9 janv. 2024
Date de fin: : 4 avril 2024
Nombre de crédits - calcul scientifique: 8
Nombre de crédits - science des données: 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.

Enseignants: Alexey Fedoseev, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 10 janv. 2023
Date de fin: : 6 avril 2023
Nombre de crédits - calcul scientifique: 8
Nombre de crédits - science des données: 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.

Enseignants: Alexey Fedoseev, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 11 janv. 2022
Date de fin: : 7 avril 2022
Nombre de crédits - calcul scientifique: 8
Nombre de crédits - science des données: 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.

Enseignants: Alexey Fedoseev, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 12 janv. 2021
Date de fin: : 1 avril 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.

Enseignants: Alexey Fedoseev, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 8 janv. 2020
Date de fin: : 3 avril 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.

Enseignants: Alexey Fedoseev, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 9 janv. 2019
Date de fin: : 5 avril 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.

Enseignants: Marcelo Ponce, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 5 janv. 2018
Date de fin: : 6 avril 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.

Enseignants: Marcelo Ponce, Erik Spence
Catégorie: Data Science (University Credit)
Date de début: : 4 janv. 2017
Date de fin: : 31 mars 2017