Spring 2024
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
Registration: Click Here to Register
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, 9am-12pm
Format: Online (Zoom)
Cost: $45
Eligible for a Digital Credential: Yes
Registration: Click Here to Register
Introduction to R
Instructors: Victoria Celio (vcelio@yorku.ca) & Gabriel Crone (gcrone14@gmail.com)
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. In this course, you will learn the following: 1) Install R and set up a working directory; 2) Enter, import, and manipulate simple data (e.g., Load packages, import different file types); 3) Create basic graphs (e.g., Bar graph, histogram, scatterplot); 4) Carry out basic analyses (e.g., t-test, correlation, regression); 5) Gain familiarity and experience using highly common R syntax, such as creating or formatting a data column based on a condition; 6) Integrate R knowledge and use R to perform data formatting and analysis through a walkthrough of an empirical research project example; 7) A brief intro to R Markdown - Incorporate your R code, results, interpretations, and notes in one document. The course is intended for anyone who has little to no experience with R. No prior programming experience is necessary.
Date/Time: TBA
Format: Online
Cost: $TBA
Eligible for a Digital Credential/Badge: Yes
Registration: Coming Soon!
Some courses offered through the QMWS permit students to receive a Digital Credential/Badge for completing the course. There is no cost to you in order to receive the Digital Credential/Badge. For more details please see Digital Credentials.
