Blog Post

Build your data literacy skills with training videos from Statistics Canada

By: Marco Campana
May 3, 2021

Statistics Canada seems like a legit place to start building your data literacy competencies:

"As Canada's national statistical organization, Statistics Canada is committed to sharing our knowledge and expertise to help all Canadians develop their data literacy skills. The goal is to provide learners with information on the basic concepts and skills with regard to a range of data literacy topics.

The training is aimed at those who are new to data or those who have some experience with data but may need a refresher or want to expand their knowledge. We invite you to check out our Learning catalogue to learn more about our offerings including a great collection of short videos. Be sure to check back regularly as we will be continuing to release new training."

Check out the Learning Catalogue for a series of self-directed training videos "organized by the step on the data journey and can be sorted by title, data competency, level, and type."

Below I've copied and pasted Statistics Canada's key stages of the data journey and core competencies.

Steps of the data journey

Steps of the data journey
Description: Steps of the data journey

The data journey represents the key stages of the data process. The journey is not necessarily linear; it is intended to represent the different steps and activities that could be undertaken to produce meaningful information from data.

Step 1: Find, gather, protect

The first step is to get data, whether this is using a pre-established database or establishing what variables are needed and creating and implementing a collection method. Security measures should be established and implemented to protect the integrity of the data once it's been collected.

The following competencies apply to this step: data discovery, data gathering, and data management and organization.

Step 2: Explore, clean, describe

Data should be explored to understand the format and variables and also checked for for errors and missing values. It may be necessary to clean the data before using it for analysis which includes doing such things like correcting formatting, removing or correcting erroneous data, or something as simple as taking out extra space. It important to document what you found and what you did to clean the data.

The following competencies apply to this step: data cleaning, and data exploration.

Step 3: Analyze, model

The purpose of doing analysis and modeling is to use statistical techniques to turn the data into information to provide meaningful insights. Analysis and modelling is used to describe a phenomenon, draw conclusions about a population or make predictions about future events.

The following competencies apply to this step: data analysis, data modelling, and/or evaluating decisions based on data.

Step 4: Tell the story

The statistical information that comes from analysis and modeling is easier to digest if it is presented in some sort of story. It could be a research paper, an infographic, an article for the media, or some combination of these and other data presentation methods.

The following competencies apply to this step: data interpretation, data visualization and/or storytelling.

Foundation: stewardship, metadata, standards and quality

In order to successfully follow the steps of the data journey, it is essential to build your work on a solid foundation of stewardship, metadata, standards and quality.

  • Stewardship encompasses all activities to govern, safeguard and protect data.
  • Metadata should describe all the processing and manipulation that the data has undergone.
  • Standard methods, practices and classifications should be applied throughout.
  • Quality should be proactively managed throughout the process and relevant quality indicators should accompany all deliverables.

Data literacy competencies

Data literacy competencies are the knowledge and skills you need to effectively work with data.

Data analysis
The knowledge and skills required to ask and answer a range of questions by analyzing data including developing an analytical plan; selecting and using appropriate statistical techniques and tools; and interpreting, evaluating and comparing results with other findings.

Data awareness
The knowledge required to know what data is and what are different types of data. This includes understanding the use of data concepts and definitions.

Data cleaning
The knowledge and skills to determine if data are 'clean' and use the best method and tools to take necessary actions to resolve any problems to ensure data are in a suitable form for analysis.

Data discovery
The knowledge and skills to search, identify, locate and access data from a range of sources related to the needs of an organization.

Data ethics
The knowledge that allows a person to acquire, use, interpret and share data in an ethical manner including recognizing legal and ethical issues (e.g., biases, privacy).

Data exploration
The knowledge and skills required to use a range of methods and tools to learn what is in the data. The methods include: summary statistics; frequency tables; outlier detection; and visualization to explore patterns and relationships in the data.

Data gathering
The knowledge and skills to gather data in simple and more complex forms to support the gatherer's needs. This could involve the planning, development and execution of surveys or gathering data from other sources such as administrative data, satellite or social media data.

Data interpretation
The knowledge and skills required to read and understand tables, charts and graphs and identify points of interest. Interpretation of data also involves synthesizing information from related sources.

Data management and organization
The knowledge and skills required to navigate internal and external systems to locate, access, organize, protect and store data related to the organization's needs.

Data modeling
The knowledge and skills required to apply advanced statistical and analytic techniques and tools (e.g. regression, machine learning, data mining) to perform data exploration and build accurate, valid and efficient modelling solutions that can be used to find relationships between data and make predictions about data.

Data stewardship
Knowledge and skills required to effectively manage data assets. This includes the oversight of data to ensure fitness for use, the accessibility of the data, and compliance with polices, directives and regulations.

Data tools
The knowledge and skills required to use appropriate software, tools, and processes to gather, organize, analyze, visualize and manage data.

Data visualization
The knowledge and skills required to create meaningful tables, charts and graphics to visually present data. This also includes evaluating the effectiveness of the visual representation (i.e., using the right chart) while ensuring accuracy to avoid misrepresentation.

Evaluating data quality

The knowledge and skills required to critically assess data sources to ensure they meet the needs of an organization. This includes identifying errors or problems and taking action to correct them. This also includes awareness of organizational policies, procedures and standards to ensure good quality data.

Evaluating decisions based on data
The knowledge and skills required to evaluate a range of data sources and evidence in order to make decisions and take actions. This can include monitoring and evaluating the effectiveness of policies and programs.

Evidence based decision-making
The knowledge and skills required to use data to help in the decision-making and policy making process. This includes thinking critically when working with data; formulating appropriate business questions; identifying appropriate datasets; deciding on measurement priorities; prioritizing information garnered from data; converting data into actionable information; and weighing the merit and impact of possible solutions and decisions.

Metadata creation and use
The knowledge and skills required to extract and create meaningful documentation that will enable the correct usage and interpretation of the data. This includes the documentation of metadata which is the underlying definitions and descriptions about the data.

The knowledge and skills required to describe key points of interest in statistical information (i.e., data that has been analyzed). This includes identifying the desired outcome of the presentation; identifying the audience's needs and level of familiarity with the subject; establishing the context; and selecting effective visualizations.

Leave a Reply

Your email address will not be published. Required fields are marked *


Please take this short 7-question survey where you can tell us how we are doing and how we might do better. This survey is anonymous. Your feedback will be used to improve the website. Thank you for your feedback! (click on the screen anywhere (or on the x in the top right corner) to remove this pop-up)