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Line 285: − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/resources Data science resources from the Data Science Network for the Federal Public Service]</h3> − <p>For data science enthusiasts: Find resources, training, tools, and communities.</p>
Discover more about data (view source)
Revision as of 09:49, 7 February 2022
, 09:49, 7 February 2022no edit summary
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<h2>Articles</h2>
<h2>Articles and posts</h2>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network/data-visualizations Creating Compelling Data Visualizations]</h3>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network/data-visualizations Creating Compelling Data Visualizations]</h3>
<p class="author">by Alden Chen, Statistics Canada</p>
<p class="author">by Alden Chen, Statistics Canada</p>
<p>Data visualization is a key component in many data science projects. For some stakeholders, especially subject matter experts and executives who may not be technical experts, it is the primary avenue by which they see, understand and interact with data projects. Consequently, it is important that visualizations communicate insights as clearly as possible. But too often, visualizations are hindered by some common flaws that make them difficult to interpret, or worse yet, are misleading. This article will review three common visualization pitfalls that both data communicators and data consumers should understand, as well as some practical suggestions for getting around them.</p>
<p>Data visualization is a key component in many data science projects. For some stakeholders, especially subject matter experts and executives who may not be technical experts, it is the primary avenue by which they see, understand and interact with data projects. Consequently, it is important that visualizations communicate insights as clearly as possible. But too often, visualizations are hindered by some common flaws that make them difficult to interpret, or worse yet, are misleading. This article will review three common visualization pitfalls that both data communicators and data consumers should understand, as well as some practical suggestions for getting around them.</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/resources Data science resources from the Data Science Network for the Federal Public Service]</h3>
<p>For data science enthusiasts: Find resources, training, tools, and communities.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<!--<h3 style="text-decoration:none;">That</h3>
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<h3 style="text-decoration:none;">[https://www.data.gov/ US Government open data]</h3>
<h3 style="text-decoration:none;">[https://www.data.gov/ US Government open data]</h3>
<p>Find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.</p>
<p>Find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
