EES1137

Qualitative Applications for Data Analysis
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Courses tagged with "EES1137"

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 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 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 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 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 2025)

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: 7 Jan. 2025
End date: 3 Apr. 2025
Scientific Computing Credits: 8
Data Science Credits: 28

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

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 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
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