Data literacy training learning catalogue

This video is intended for learners who want to acquire a basic understanding of analysis. No previous knowledge is required.

Now that we've learned how to plan an analytical project, we'll discuss best practices for implementing your plan. Please watch "Analysis 101, part 1" before you start.

Now that we've learned how to plan an analytical project and perform the analysis, we'll discuss best practices for interpreting and sharing your findings.

In this video, we will review the steps of the analytical process and you will obtain a better understanding of how analysts apply each step of the analytical process by walking through an example. Please watch the previous Analysis 101 videos before you start.

By the end of this video, you will have a better understanding of why data tables are important, how data tables are structured and how to interpret data quality indicators within a table.

These videos are a reference source for users who are new to census concepts or those who have some experience with these concepts, but may need a refresher or would like to expand their knowledge.

In this video we will present methods to describe accuracy in terms of validity and correctness.

What are ethical reviews? How do ethical reviews help Statistics Canada apply data ethics principles throughout the data journey? Using a case example, this video will show you how Statistics Canada uses ethical reviews to apply data ethics principles throughout the data journey.

An introduction to data ethics, why they are important, and the 6 guiding principles of data ethics implemented by Statistics Canada.

In this video you will learn about the steps and activities in the data journey, as well as the foundation supporting it.

This course is intended for Government of Canada employees who would like to learn about evaluating the quality of data for a particular use.

An introduction to the fundamentals of data quality, which can be summed up in six dimensions—or six different ways to think about quality. Learn how each dimension can be used to evaluate the quality of data.

This toolkit raises awareness about data quality assurance practices.

Data gathering involves first determining what data you need, then where to find it, how to get it and how to keep it safe. This video introduces you to things you should consider when gathering data.

Whether you're gathering new data or using existing data, applying data standards will make your life easier. And documenting your data in metadata will ensure that others will be able to find it, understand it, and use it.

When used properly, charts (including figures and diagrams) can simplify the presentation of information and the communication of clear and precise messages. However, with the wide range of options available, creating effective charts can be complex. This reference tool is intended to provide a basic guide to creating effective charts that take advantage of the options available.

This video will provide an introductory overview of data visualization and how you might be able to use it to tell your story.

What does it means to be FAIR in terms of data and metadata, and how does each pillar of FAIR serve to guide data users and producers as they navigate their way through the data journey.

This document is a handbook for practitioners developing and implementing Machine Learning (ML) processes. It provides guidance and practical advice on how to responsibly develop these automated processes.

Data gathering involves first determining what data you need, then where to find it, how to get it and how to keep it safe. This video introduces you to things you should consider when gathering data.

Workshop on statistical measures such as percentages, averages, frequency and standard error.

Welcome to machine learning: an introduction. Here, we will explain the basic concepts of machine learning, and include a framework for responsible machine learning processes.

Workshop on probability survey results, including: impact of survey process on results, how to review data, when to use survey weights, and presenting the story.

Follow along with these tutorials and learn the key concepts and procedures for performing common GIS tasks - such as creating maps, joining and overlaying datasets, and examining spatial variations.

What are confidence intervals? Why do we use confidence intervals? What factors have an impact on a confidence interval?

This video is intended for viewers who wish to gain a basic understanding of correlation and causality. As a prerequisite, before beginning this video, we highly recommend having already completed our videos titled "what is data" and "types of data".

This video is intended for learners who want to acquire a basic understanding of the concept of central tendency, what it means and some key related methods used for the exploration of data.

By the end of this video, you should have a basic understanding of such measures of dispersion as range, interquartile range, standard deviation.

This video will introduce you to the concepts of proportions, ratios, and rates – and the differences between them.

In this video, we will explain the concept of statistical bias, which occurs when statistics differ systematically from the reality they are trying to measure because of problems with the way the data were produced.

This web resource was created to assist secondary students and teachers in getting the most from statistics.

Workshop on the steps of the survey process including questionnaire wording, sample design and interpreting survey results.

This video is meant to help learners understand how to tell interesting and informative stories with data.

Workshop on using statistics to present your story and put numbers into a context to help your audience understand your messages.

It is important to define the different types of data and understand them in order to choose the appropriate method for analyzing the data and presenting the results.

This video will introduce some basic terminology and concepts related to data.