SciNet and Graduate Education

By partnering with other departments in the University, an increasing number of SciNet’s training courses have been converted into graduate courses at the University of Toronto. Our current partners include UofT’s Departments of Physics, Astrophysics, Chemistry, and Ecology and Evolutionary Biology and the Institute for Medical Sciences, but the courses also draw students from other faculties such as Engineering.

Indicative of the success of these “partnered” courses, the full term physics graduate course “Scientific Computing for Physicists” has had a consistent enrollment of around 40 in the last three Winter terms since its creation in 2016, and attracted students from many different departments such as physics, astrophysics, engineering and math. The modular course Data Analysis with R, given in partnership with IMS and EEB, which started in the Fall of 2016, had over 100 registered students, a success which prompted the creation of a full term IMS course, “Introduction to Clinical BioStatistics”. This course has a large data science component and has had no problems filling up the enrollment cap of 80 students. SciNet analysts also deliver a graduate course “Quantitative Applications for Data Analysis” in partnership with the Biological Sciences group at University of Toronto Scarborough. Furthermore, since 2017, SciNet analysts also guest-lecture in the 4th year Physics undergraduate Research Project course.

Below are the links to the webpages of latest versions of these courses, which include lecture notes and recordings.

PHY1610H Scientific Computing for Physicists

This course is aimed at reducing the 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.

This course has been offered in the academic years 2015-2016, 2016-2017, 2017-2018, and 2018-2019, and has been taken by 140 students.

From 2011 to 2015, this content was provided as modular courses within PHY2109YH and AST 3100H. These modular courses were taken by about 60 students.

EES1137H Quantitative Applications for Data Analysis

The goal of this course is to prepare graduate students to perform scientific data analysis. 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 practices to store, manage and analyze (large) data. Techniques utilizing Python and R statistical language will be discussed and introduced, as well as the basics of programming and scientific computing.

This course is taught at the Scarborough campus of the University of Toronto and has been offered in the academic years 2016-2017, 2017-2018, and 2018-2019. It has been taken by 72 students.

MSC1090H Intro to Computational BioStatistics with R

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.

This course has been offered in the academic year 2016-2017 (twice) and 2018-2019. It has been taken by 183 students.

Before that, a precursor of this course existed as the “Intro to Data Analysis with R” module within “MSC1010Y Seminars in Translational Research”, and was taken by 66 students.

Guest lectures for