Spring 2024
Introduction to Survival Analysis
Instructor: Kevin McGregor, PhD (kevinmcg@yorku.ca)
Course Description: Survival analysis, also known as time-to-event analysis, is an important branch of epidemiology and biostatistics. This short course will cover the basics of survival analysis and some of the most common analytical techniques used in this field. We will begin with the ideas of censored and truncated data. Then we will proceed to discuss various non-parametric methods such as the Kaplan-Meier estimator. Next we will discuss semi-parametric models including proportional hazards models as well as time dependent covariates. R will be used extensively in this course. Attendees are expected to have at least a basic knowledge of R and a basic knowledge of statistics.
Dates/Times: May 7, 14, & 21, 9:00 am - 12:00 pm
Format: Online (Zoom)
Cost: $45
Eligible for a Digital Credential: Yes
A Crash Course on Statistics
Instructor: Cathy (Xijuan) Zhang, PhD (xijuan@yorku.ca)
Course Description: This is a 4-hour introductory crash course on statistics. No prior knowledge is required! The course will start with a discussion on the purpose and history of statistics. Then I will walk you through a variety of important statistics concepts and analyses, covering probability theories, hypothesis testing, regression analyses, and machine learning techniques. I will also include an introduction to the R programming language, which is commonly used for statistical analyses. Whether you are curious about statistics or preparing for a formal course on statistics, this crash course will provide you with valuable insights and a solid foundation to jumpstart your statistical learning.
Dates/Times: Wed May 29th & Friday May 31th, 2pm-4pm
Format: Online (Zoom)
Cost: $20
Eligible for a Digital Credential: Yes
Introduction to R
Instructors: Gabriel Crone (gc2001@my.yorku.ca); Victoria Celio (celio12@my.yorku.ca); Ken Suzuki (Ken100@my.yorku.ca)
Course Description: R is a free and open-source statistical programming language that is widely used in academia for data analysis and producing insightful graphics with ease. It has the added benefit of being free! You can download it anytime (see here for a guide). In this three-part workshop series, we will gain familiarity with coding in R, with a focus on describing and analyzing real-life data. We will be engaging with the content through live demos, interactive activities, and practice examples—and all materials will be based on real data! In terms of topics, during this workshop, you will learn the basics of R (day 1), how to complete data manipulation and run descriptive statistics (day 2), and how to conduct quality statistical analyses within R via linear regression analyses (day 3). The course is intended for anyone who has little to no experience with R. No prior programming experience is necessary. We look forward to learning R togetheR!
Dates/Times: June 6, 13, & 20, 11:30 AM - 2:30 PM
Format: Online
Cost: $45
Eligible for a Digital Credential/Badge: Yes
