Calendar

DESCRIPTION: Plotting and data visualization are essential for effectively communicating bioinformatics findings, yet they are often treated as trivial tasks. In this course, we will showcase the power of a well-designed plot! We will cover key principles of effective visualization, work through examples ranging from basic to complex, and conclude with a hands-on workshop. By the end of the course, you will be able to create publication- or presentation-ready plots for your own research using R and ggplot2.

TEACHER: Rachel Edgar (Bioinformatics.ca, UHN)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES:

* Basic knowledge of R.
* Basic knowledge of working in R-Studio.

DESCRIPTION: This workshop is a primer for those largely new to supercomputing, i.e., to computing on shared, remote resources. It is intended to demystify the somewhat intimidating term "High-Performance Computing" (HPC), and to serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, available resources, essential issues, and a high level overview of parallel programming models commonly used on these systems.

TEACHER: Ramses van Zon (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATES: Attendance and Completion

PREREQUISITE: Basic Linux (e.g., "Introduction to Linux Shell" course)

DESCRIPTION: This half-day session offers a brief introduction to R, with a focus on data analysis and statistics. We will discuss the following topics: the R interface, primitive data types, lists, vectors, matrices, and data frames - a crucial data type in data analysis and the trademark of the R language. Advanced topics to be covered include: basics statistics and function creation; and the basics of scripting.

TEACHER: Alexey Fedoseev (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES: Some programming experience in another programming language

DESCRIPTION: This course will provide an overview of the analytical process for working with neurophysiological data and deriving insights from it. We will explore preprocessing, feature extraction, and downstream analysis (e.g., machine learning) end-to-end with discussions throughout on considerations for analysis based on theory and empirical observations.

TEACHERS: Irene Harmsen (Cove Neuro, OBI Centre for Analytics)

LEVEL: Intermediate

FORMAT: Webinar

CERTIFICATES: Attendance and Completion.

PREREQUISITES:

* Knowledge of time-series data analysis.
* Python programming experience.
* Basic understanding of statistics and machine learning.