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Line 79: − + − − [[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]] − <h3 style="text-decoration:none;">Numbersense: How to Use Big Data to Your Advantage</h3> − <p class="author">by Kaiser Fung</p> − <p>We live in a world of Big Data--and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it--whether we realize it or not. − − The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations. − − <i>Numbersense</i> gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician, but you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.</p> − <p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p> − <br> Line 96:
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Discover more about data (view source)
Revision as of 11:30, 30 January 2022
, 11:30, 30 January 2022no edit summary
<h2>Data Conference 2022 Bookshelf</h2>
<h2>Books and reports</h2>
[[Image:SCC_Data_Gov_Roadmap_EN_COVER.png |150px|Canadian Data Governance Standardization Collaborative Roadmap]]
[[Image:SCC_Data_Gov_Roadmap_EN_COVER.png |150px|Canadian Data Governance Standardization Collaborative Roadmap]]
<p class="author">by the Canadian Data Governance Standardization Collaborative</p>
<p class="author">by the Canadian Data Governance Standardization Collaborative</p>
<p>The Canadian Data Governance Standardization Roadmap tackles the challenging questions we face when we talk about standardization and data governance. It describes the current and desired Canadian standardization landscape and makes 35 recommendations to address gaps and explore new areas where standards and conformity assessment are needed.</p>
<p>The Canadian Data Governance Standardization Roadmap tackles the challenging questions we face when we talk about standardization and data governance. It describes the current and desired Canadian standardization landscape and makes 35 recommendations to address gaps and explore new areas where standards and conformity assessment are needed.</p>
<p>SCC established the Canadian Data Governance Standardization Collaborative in 2019 to accelerate the development of industry-wide data governance standardization strategies. The Collaborative spent the past two years working together to build a standardization Roadmap. The Canadian Data Governance Standardization Collaborative is a group of 220 Canadians across government, industry, civil society, Indigenous organizations, academia, and standards development organizations.</p>
<p>SCC established the Canadian Data Governance Standardization Collaborative in 2019 to accelerate the development of industry-wide data governance standardization strategies. The Collaborative spent the past two years working together to build a standardization Roadmap. The Canadian Data Governance Standardization Collaborative is a group of 220 Canadians across government, industry, civil society, Indigenous organizations, academia, and standards development organizations.</p>
<p class="recco">Recommended by the [https://www.scc.ca/ Standards Council of Canada], friend of the GC Data Community</p>
<p class="recco">Recommended by the [https://www.scc.ca/ Standards Council of Canada], friend of the GC Data Community</p>
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[[Image:Data-Feminism-cover.jpg|150px|Data Feminism, by Catherine D'Ignazio and Lauren F. Klein]]
<h3 style="text-decoration:none;">Data Feminism: A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.</h3>
<p class="author">by Catherine D'Ignazio and Lauren F. Klein</p>
<p>Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.
<p>[https://mitpress.mit.edu/books/data-feminism <i>Data Feminism</i>] offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
<br>
[[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]]
<h3 style="text-decoration:none;">Numbersense: How to Use Big Data to Your Advantage</h3>
<p class="author">by Kaiser Fung</p>
<p>We live in a world of Big Data--and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it--whether we realize it or not. The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations.</p>
<p><i>Numbersense</i> gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician, but you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.</p>
<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>
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<h2>Articles</h2>
<h3 style="text-decoration:none;">This</h3>
<p>Description</p>
<h3 style="text-decoration:none;">That</h3>
<p>Description</p>
<h2>Websites to check out</h2>
<h2>Websites to check out</h2>
