Difference between revisions of "Discover more about data"
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<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> | <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 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>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 <i>Data Feminism</i>, 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>[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> | ||
| + | |||
| + | [[Image:Invisible-Women-cover.jpg|150px|Invisible Women: Data Bias in a World Designed for Men, by Caroline Criado Pérez]] | ||
| + | <h3 style="text-decoration:none;">Invisible Women: Data Bias in a World Designed for Men</h3> | ||
| + | <p class="author">by Caroline Criado Pérez</p> | ||
| + | <p>Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives.</p> | ||
| + | <p>Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in <i>Invisible Women</i>, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.</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> | ||
Revision as of 11:40, 30 January 2022
Register now Conference agenda Conference speakers
Brought to you by Statistics Canada and the Canada School of Public Service with support from the GC Data Community
Discover more about data
Learning
Digital Academy's Discover Series: Discover Data
An introduction to data in the Government of Canada: Discover how the data revolution is shaping government today and how the effective use of data creates opportunities to improve programs and services for Canadians.
How Data Literate Are You? (online, self-paced)
A Self-Directed Guide to Understanding Data (online, self-paced)
The Role of Data in Digital Government (virtual classroom)
Books and reports
Canadian Data Governance Standardization Collaborative Roadmap (June 2021)
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.
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.
Recommended by the Standards Council of Canada, friend of the GC Data Community
Data Feminism: A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
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.
Data Feminism 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.
Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner
Invisible Women: Data Bias in a World Designed for Men
Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives.
Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in Invisible Women, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.
Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner
Numbersense: How to Use Big Data to Your Advantage
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.
Numbersense 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.
Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner
Articles
Creating Compelling Data Visualizations
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.
Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner
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