Biomedical and biochemistry

Courses for medical and biochemistry students
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Courses tagged with "Biomedical and biochemistry"

BCH2203 Introduction to Programming in Python (Winter 2024)

In this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. 

This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI.

Teacher: Ramses van Zon
Category: Scientific Computing (University Credit)
Start date: 10 Jan 2024
End date: 17 Apr 2024
Scientific Computing Credits: 9

BCH2024 Introduction to Programming with R (Winter 2021)

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.
Teacher: Marcelo Ponce
Category: Scientific Computing (University Credit)
Start date: 22 Feb 2021
End date: 10 May 2021

BCH2024 Introduction to Programming with R (Apr/May 2020)

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.
Teacher: Marcelo Ponce
Category: Scientific Computing (University Credit)
Start date: 14 Apr 2020
End date: 21 May 2020

MSC1090 Introduction to Computational Biostatistics with R (Fall 2024)

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, 10:00-11:30am, in VC323.

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: 10 Sep 2024
End date: 28 Nov 2024
Scientific Computing Credits: 8
Data Science Credits: 28

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

BCH2202 Introduction to Programming in R (Winter 2023)

In this course students will be instructed in how to program in R. Ultimately students will learn how to use R to analyze, process and visualize data. This course is designed for students with little to no experience in programming. 

This is a graduate course that can be taken for credit by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI.

Teacher: Erik Spence
Category: Data Science (University Credit)
Start date: 8 Feb 2023
End date: 26 Apr 2023
Scientific Computing Credits: 9
Data Science Credits: 9

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

BCH2203 Introduction to Programming in Python (Winter 2022)

In this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. 

This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI.

Teacher: Ramses van Zon
Category: Data Science (University Credit)
Start date: 12 Jan 2022
End date: 15 Apr 2023
Scientific Computing Credits: 9
Data Science Credits: 9

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

BCH2024 Introduction to Programming in Python for Biochemistry (Sept. 2020)

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed.
Teachers: SciNet Team, Ramses van Zon
Category: Data Science (University Credit)
Start date: 8 Sep 2020
End date: 21 Oct 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