Skip to main content

Self-paced modules and recorded workshops

Take a self-paced module or watch previously recorded workshops on the topics listed below. 

Self-paced modules

Library Research Skills 

Would you like to get CCR credit for the Library Research Skills modules? 
If so, complete the CCR edition 

This series of six modules will introduce students to foundational library skills that will help in many university research assignments. Each module is a stand-alone resource. They can be completed in any order, all at the same time, or individually as needed. Topics include:

  • Module 1: Introduction to U of T Libraries - Learn the basics about the U of T libraries
  • Module 2: Start Your Research - Learn how to get started with your library research project
  • Module 3: Select Your Sources - Learn how to find the best sources for your assignment
  • Module 4: Search Tools - Learn how to find the best search tools for your assignment
  • Module 5: Search Effectively - Learn how to search effectively 
  • Module 6: Evaluate Your Sources - Learn how to evaluate sources properly

Time commitment: 20 minutes per module.

Introduction to Data Visualization – Part 1: Theory and Critique

Through a combination of videos, web pages, quizzes, and activities, this self-paced online course will use a data visualization design workflow model to introduce participants to best practices and guidelines for designing effective visualizations and evaluating visualizations. For more information on Data Visualization, including topics covered in the course, and services offered by the libraries, see our Data Visualization Guide.

Time commitment: 1 hour, 15 minutes. There is an additional 1 hour, 30 minutes (approx.) of activities.

Introduction to Data Visualization – Part 2: Practice with Tableau

Through a combination of videos, web pages, quizzes, and activities, this self-paced online course will introduce participants to a common data visualization tool, Tableau Desktop. Participants will learn to create a variety of visualizations such as a line graph of profits over time by product category, a box plot of iris petal widths by species, and a stacked bar graph of word frequencies in Romeo and Juliet. For more information on Data Visualization, see our Data Visualization Guide.

Time commitment: 2 hours. There is an additional 1 hour (approx.) of activities.

Introduction to Infographics

Through a combination of lecture and activities, this self-paced online course will introduce participants to best practices and guidelines for designing effective infographics and evaluating them. Participants will get a chance to go through the entire design process of an infographic, from determining the story, sketching the layout, choosing appropriate data visualizations, selecting fonts and colours, and finally using a common online infographic creation tool called Piktochart, to implement the design and create a finished infographic. For more information on Data Visualization, including topics covered in the workshop, and services offered by the libraries, see our Data Visualization Guide.

Time commitment: 45 minutes. There is an additional 1 hour and 30 minutes (approx.) of activities.

Working with Messy Data in OpenRefine

Through a combination of videos, web pages, quizzes, and activities, this self-paced online course will provide an introduction to OpenRefine, a powerful open source tool for exploring, cleaning and manipulating “messy” data. Through demonstrations and 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.

Time commitment:  Approximately 1 hour, 15 minutes. There is an additional 1 hour (approx.) of activities.

Introduction to R

This course is broken down into multiple sequential modules that consist of videos, activity and quizzes. The following topics are covered:

  • Data types and Data structures
  • Importing and Exporting data
  • Exploring Data
  • Graphs
  • Creating Variables
  • Managing Data
  • Tidyverse
  • R Markdown

Time commitment: Approximately 2 hours.

Navigating the COVID-19 Evidence Landscape

This module is intended to: 
1. Describe different types of information sources and the evidence processing involved in creating them
2. Understand the unique special considerations to be aware of when searching for and using evidence for Covid-19

Time commitment: 30-45 minutes.

Copyright for Teaching and Learning

This open course provides U of T faculty, students, and staff with a general overview of copyright through seven short, self-directed instructional modules. These modules are intended to help the U of T community ensure that their professional activities comply with Canadian copyright law.

Time commitment: 10 minutes per module.
 

Recorded workshops

Getting Stared with LibrarySearch

Learn how to find and access books and articles in LibrarySearch, U of T Libraries' new library search platform.
Duration: 27 minutes


Scholarship in Conversation: Accidental Plagiarism 

We understand that sometimes plagiarism can be unintentional and we want to help you develop strategies and tools to recognize the ways in which plagiarism can be prevented. Setting you up for success for this new academic journey is what we are here to do!
Join us for a virtual workshop where we will discuss:

  • Smart Strategies
  • Style Guides
  • Citing in Academic Writing
  • Reference Software Management
  • And much more!

Duration: 1 hour, 40 minutes
 

Map & Data Library Workshops

The Map & Data Library offers a number of workshop recordings, for these workshops and more:

  • Constellate: A new platform to learn text analysis
  • Digital Humanities Tools: the Digital Scholar Lab
  • Introduction to Data Visualization Using Tableau Desktop
  • Introduction to Infographics
  • Introduction to NVivo 12 (Windows and Mac Versions)
  • Network Analysis and Visualization Using Gephi - An Introduction
  • Introduction to R
  • Introduction to Stata
  • Text Analysis Tasting Menu: A Sampling of Available Tools
  • Working with Messy Data in OpenRefine