The Institute’s Statistical Consulting Service (SCS) offers short courses on various aspects of statistics and statistical computing, including regular introductions to commonly used statistical packages, three times a year (Fall, Winter and Spring). Recent course offerings have addressed factor analysis, structural equation modeling, graphical methods for categorical data, linear regression and mixed models.

The Statistical Consulting Service maintains a regular schedule of office hours during the academic year. The Service primarily serves the York University community; for others, consultation is available on a fee-for-service basis. Please go to the Institute’s SCS website to make appointments online with SCS consultants.

Pre-registration and payment of fees is required for all 2020 Fall Seminar Courses

SCS SHORT COURSES, October-November 2020

Data Analytics and Machine Learning using R

This course is full, registration is for waiting list only.

Instructor: Professor Xin Gao
Dates: Mondays, October 5, 19 and 26, and November 2, 2020
Time: 1:00pm-4:00pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 10
Enrolment Minimum: At least five registrants are required in order to hold the course.

Course Description:  Date analytics is the science of analyzing data to draw conclusions, build models and make predictions. The aim of this course is to provide an application-oriented training on data analytics in an academic or business setting. The course will cover a wide selection of data analytic techniques to equip the participants with appropriate computing skills to conduct machine learning and data mining. The lectures will cover various methodologies and algorithms, as well as teach students how to use data analytics-related software (R) to solve real-life problems. The Course Director will provide case studies to practise the analytical skills covered in the course. The course will cover logistic regression, Naïve Bayes Estimation, the K-nearest neighbour algorithm, and hierarchical and k-means clustering, classification and decision trees, neural networks and application of neural network modeling. The course will teach the participants how to use R, but some prior knowledge of R would be very useful to benefit fully from the course.

About the Instructor:  Xin Gao is a Professor in the Department of Mathematics and Statistics at York University. She received her PhD in Statistics from the University of Ottawa in 2003. Her research interests centre around computational statistics and high dimensional data analysis.  For more details see xingao.info.yorku.ca.

Advanced Research Design Seminar

Instructor: Professor Bryn Greer-Wootten
Dates: Tuesdays, October 6, 20, November 3 and 17, 2020
Times: 6:00pm-9:00pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 10

Course Description:  Research design in the social, environmental and behavioural sciences today must consider the choices to be made between quantitative, qualitative and mixed (i.e., both quantitative and qualitative) methods. This short course is designed as a seminar to examine such choices. An introductory presentation distinguishes between these approaches from philosophical perspectives. Subsequent sessions discuss: (i) the primary issues, based on assigned readings; (ii) critical reviews of participant-chosen research articles; and (iii) group critique of individual research proposals. Sufficient time between meetings (two weeks) is allowed for the work required for these activities.

Enrolment is limited to 10 in order to maximize the seminar setting, even in using remote methods. This short course is open to everyone, but the participant likely to gain most from the experience is a PhD candidate post-comprehensives or a junior faculty person.

It may be necessary to select participants based on their applications: please be sure to enter your reasons for applying for this Short Course in the online Registration Form in the box marked “Additional Information”.

Applicants will be notified of acceptance one week prior to the first seminar meeting, i.e., by September 29, 2020.

About the Instructor: Bryn Greer-Wootten is Professor Emeritus in Environmental Studies and Geography at York University. In 2002 he joined the staff of the Statistical Consulting Service, where he is currently an Associate Coordinator, and in 2004 was appointed an Associate Director of ISR. He has taught and carried out quantitative, qualitative and mixed methods research, with a particular interest in survey research and survey data analysis problems, for over fifty years.

Social and Health Survey Data Sources and Emerging Data Collection Methods

Instructor: Hugh McCague, PhD
Dates: Tuesdays, October 20 and 27, 2020
Time: 9:00am – 12:00pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 20
Enrolment Minimum: At least five registrants are required in order to hold the course.

Course description: This Short Course provides participants with an overview of some of the major social and health survey data sources and emerging data collection methods: Statistics Canada and Research Data Centres, government and administrative data, respondent-driven sampling, Google search data through Google Trends, web scraping, Wiki surveys, social media data, wearables and smartphone apps. The integration of different data sources to obtain a more informative context is also highlighted. An in-depth “how to” is not given on any individual data source area, but resources are provided on how to take the next steps. Part of the value of the Short Course is raising awareness of the major changes that survey research is undergoing, strengths and issues with different data sources, and new opportunities for research. In-class exercises provide practice in accessing some of the data sources.

 About the Instructor: Hugh McCague is a statistician at the Institute for Social Research and Statistical Consulting Service at York University. His work and research concentrate on applications of statistics in health and environmental studies, including the use of data at the Statistics Canada Research Data Centre at York University, as well as the on-going public health surveys of the Institute.

Visualizing Linear Models: An R Bag of Tricks

Instructor: Professor Michael Friendly
Dates: Thursdays: Oct 22, 29, Nov 5, 2020
Time: 1:00pm – 4:00pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 20
Enrolment Minimum: At least five (5) registrants are required in order to hold the course.

Course description:  OK, so you ran your ANOVA, multiple regression (MRA), or multivariate counterparts (MANOVA, MMRA), but now you need to visualize the results to both understand them and communicate.  Who you gonna run to? – R of course.

This course covers data visualization methods designed to convert models and tables into insightful graphs.  It starts with a review of graphical methods for univariate linear models—data plots, model (effect) plots and diagnostic plots. A brief introduction to multivariate linear models uses data ellipses (or ellipsoids) as visual summaries of 2D (or 3+ D) multivariate relations.  The Hypothesis-Error (HE) framework provides a set of tools for visualizing effects of predictors in multivariate linear models. I give some examples of HEplots for MANOVA and MMRA designs. Finally, if time permits, some model diagnostic plots for detecting multivariate outliers and lack of homogeneity of (co)variances will be described.

Participants should have a background in statistics including a course in linear models (ANOVA, multiple regression). In addition, they should have some familiarity with using R and R Studio, such as the SCS course, An Introduction to R and the Tidyverse, or equivalent.  A web page for the course will give access to lecture notes and resources.

About the Instructor: Michael Friendly is a Joint-Coordinator of the Statistical Consulting, Professor in the Department of Psychology, and a Fellow of the American Statistical Association. In addition to his research interests in psychology, Professor Friendly has broad experience in data analysis, statistics and computer applications. He is the author of several books using SAS (SAS for Statistical Graphics, 1st Edition and Visualizing Categorical Data), and more recently of Discrete Data Analysis with R. He is also an Associate Editor of the Journal of Computational and Graphical Statistics and Statistical Science. His recent work includes the further development of graphical methods for categorical data and multivariate data analysis.

Course Fees

All fees include HST.

York students (with FAS account)
  • Data Analytics and Machine Learning using R … $122.04
  • Advanced Research Design Seminar … $122.04
  • Social and Health Survey Data Sources and Emerging Data Collection Methods … $56.50
  • Visualizing Linear Models: An R Bag of Tricks … $91.53
York faculty and staff
  • Data Analytics and Machine Learning using R … $271.20
  • Advanced Research Design Seminar … $271.20
  • Social and Health Survey Data Sources and Emerging Data Collection Methods … $56.50
  • Visualizing Linear Models: An R Bag of Tricks … $203.40
Full-time students at other post-secondary institutions
  • Data Analytics and Machine Learning using R … $212.44
  • Advanced Research Design Seminar … $212.44
  • Social and Health Survey Data Sources and Emerging Data Collection Methods … $56.50
  • Visualizing Linear Models: An R Bag of Tricks … $159.33
External participants
  • Data Analytics and Machine Learning using R … $497.20
  • Advanced Research Design Seminar … $497.20
  • Social and Health Survey Data Sources and Emerging Data Collection Methods … $56.50
  • Visualizing Linear Models: An R Bag of Tricks … $372.90

See the registration form for payment method.

Refunds are available upon three business days’ notice prior to the course start date and are subject to an administrative fee.

Please review our policy regarding refunds.

Certificate of Completion

Available on request, full attendance is required.
A $5.65 administrative fee applies, for each certificate requested.

Registration

You can register for courses by completing the on-line registration form.

You will get an e-mail confirmation for your registration.   Credit card payment will be processed by our Administrative Assistant, Betty Tai over the telephone.

Email: btai@yorku.ca or isrcours@yorku.ca

Tel: 416-736-5061 Ext. 33024

Additional Information

Additional information regarding registration, contact Institute for Social Research (ISR) by telephone at 416-736-5061 Ext. 33024 , weekdays, from 10:00am to 4:00pm