MSC1090

Introduction to computational biostatistics with R
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Courses tagged with "MSC1090"

MSC1090 Introduction to Computational Biostatistics with R (Fall 2023)

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.

Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085.

Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 12 Sep 2023
End date: 30 Nov 2023
Scientific Computing Credits: 8
Data Science Credits: 28

MSC1090 Introduction to Computational Biostatistics with R (Fall 2022)

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.

Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in GB244 and BL205, respectively.

Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 13 Sep 2022
End date: 29 Nov 2022
Scientific Computing Credits: 8
Data Science Credits: 28

MSC1090 Introduction to Computational BioStatistics with R (Fall 2020)

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.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 8 Sep 2020
End date: 26 Nov 2020

MSC1090 Introduction to Computational BioStatistics with R (Fall 2019)

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 to be taught at the UofT St. George campus.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 9 Sep 2019
End date: 5 Dec 2019

MSC1090 Introduction to Computational BioStatistics with R (Fall 2018)

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 to be taught at the UofT St. George campus.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 11 Sep 2018
End date: 29 Nov 2018

MSC1090 Introduction to Computational BioStatistics (Winter 2018)

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 to be taught at the UofT St. George campus.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 9 Jan 2018
End date: 5 Apr 2018

MSC1090 Introduction to Clinical BioStatistics (Quantitative Applications for Data Analysis) (Fall 2017)

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 to be taught at the UofT St. George campus.

Teachers: Marcelo Ponce, Erik Spence
Category: Data Science (University Credit)
Start date: 12 Sep 2017
End date: 30 Nov 2017

MSC1090 Introduction to Computational Biostatistics with R (Fall 2021)

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.

Teachers: Alexey Fedoseev, Erik Spence
Category: Data Science (University Credit)
Start date: 14 Sep 2021
End date: 2 Dec 2021
Scientific Computing Credits: 8
Data Science Credits: 28