Workshop schedule

Winter 2018 Workshops

Finding Canadian Statistics

This workshop will introduce you to sources of Canadian statistics, with a focus on tools for discovering Statistics Canada data, including CANSIM, CHASS Census Analyser, and the Map & Data Library catalogue. It will cover search strategies and techniques and allow hands-on time for exploring and discovering data of interest to your own research.

Finding Canadian & International Microdata

This workshop will introduces you to sources of individual-level social science data. It will cover both Canadian and international sources. It will cover search strategies and techniques and allow hands-on time for exploring and discovering data of interest to your own research.

Introduction to ArcGIS Desktop 10.5

In this workshop, attendees will learn the basics of Geographic Information Systems (GIS). Hands-on activities will cover loading, manipulating, and visualizing geospatial datasets using Esri's ArcGIS software, working towards creating digital maps.

Introduction to ArcGIS Online

In this workshop, attendees will learn the basics of web mapping and Geographic Information Systems (GIS) in an online enviornment. Hands-on activities will cover loading, manipulating, and visualizing geospatial datasets online using Esri’s ArcGIS Online software, working towards creating interactive online maps.

Introduction to Data Visualization

Through a combination of lecture and activities, this three-hour workshop will use a data visualization workflow model to introduce students to best practices and guidelines for designing effective visualizations and evaluating visualizations. For the final part of the workshop, students will get a chance to work with a common data visualization tool, Tableau Desktop, creating visualizations such as a line graph of average temperature by month, a treemap of population by world regions, and a stacked bar graph of word frequencies in Romeo and Juliet. For more information on Data Visualization, including topics covered in the workshop, and services offered by the libraries, see our Data Visualization Guide.

Introduction to NVivo

This session provides an overview of the use of NVivo for qualitative data analysis. Participants will gain a sense of the various uses of NVivo in the research process and the types of data sources that the software accommodates, as well as the basics of coding data.

  • This workshop will not be held in Winter 2018 (sorry!).

Introduction to SimplyAnalytics

SimplyAnalytics is a web-based mapping, analytics, and data visualization application for creating maps, charts and report using demographic, business, health and marketing data. This session will show you how to create custom maps for projects and presentations, as well as generate reports and download data to support your research.

Introduction to SPSS

This workshop provides an introduction to using SPSS to perform common data management and basic statistical analysis tasks. Participants will work through hands-on exercises in SPSS, and will learn to import spreadsheets, manage and edit data, create charts and other visualizations, and run descriptive statistical tests. Note: this workshop will not cover advanced statistical concepts, but participants will be provided with suggested resources for additional skills development.

Introduction to Stata

In this hands-on workshop, students will learn how to read, explore, manipulate and combine data in Stata as well as run some common statistical analyses. It is suitable for new Stata users and Stata users looking to develop their familiarity with common Stata syntax.

Introduction to R

This is a hands-on workshop that introduces the R statistical programming language using RStudio. The topics covered are reading, exploring, manipulating data and running some statistical tests. It is most suitable for new R users or users looking to review their knowledge in R.

Working with Messy Data in OpenRefine

This workshop will provide an introduction to OpenRefine, a powerful open source tool for exploring, cleaning and manipulating “messy” data. Through hands-on activities, using a variety of datasets, participants will learn how to explore and identify patterns in data, normalize data, transform and reshape data, and more.