Changes

}}

}}

<p style="text-align:right; padding: 10px; margin-top: -10px; width:1130px"><strong>[https://wiki.gccollab.ca/Découvrez_plus_sur_les_données Français]</strong></p>

<p style="text-align:left; padding: 10px; margin-top: -10px; width:1130px"><strong>[https://wiki.gccollab.ca/Découvrez_plus_sur_les_données Français]</strong></p>

[[Image:DataCon2022-Banner-EN.png |100%|Data Conference 2022: Driving Data Value and Insights for All Canadians, 23 + 24 February 2022]]

[[Image:DataCon2022-Banner-EN.png |100%|Data Conference 2022: Driving Data Value and Insights for All Canadians, 23 + 24 February 2022]]

<p style="background-color: #f18f34; padding: 5px; width:1130px""><small>

<p style="background-color: #f18f34; padding: 5px; width:1130px""><small>

<strong>[https://www.csps-efpc.gc.ca/events/data-conference2022/index-eng.aspx Register now]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://vexpodev.z9.web.core.windows.net/en/#/2203/lobby Virtual Expo]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda Agenda]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda Agenda]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Speakers Conference speakers]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Speakers Conference speakers]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Networking_Missions Networking Missions]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Networking_Missions Networking Missions]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/Discover_more_about_data DISCOVER MORE ABOUT DATA]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<strong>[https://wiki.gccollab.ca/GC_Data_Conference_2023/Discover_more_about_data Discover more about data 2023]</strong>&nbsp;&nbsp;|&nbsp;&nbsp;

<!--<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Announcements Announcements]</strong>-->

<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Announcements Announcements]</strong>

</small></p>

</small></p>

<p style="color: DarkBlue; font-size:large;"><strong>Brought to you by Statistics Canada and the Canada School of Public Service with support from the GC Data Community</strong></p>  

<p style="color: DarkBlue; font-size:large;"><strong>Brought to you by Statistics Canada and the Canada School of Public Service with support from the GC Data Community</strong></p>  

 

<h1>Discover more about data</h1>

<h1>Discover more about data</h1>

<p><small>Are we missing something? Send us the details: [mailto:gcdc-cdgc@csps-efpc.gc.ca GC Data Community]</small></p>

<p><small>Are we missing something? Send us the details: [mailto:gcdc-cdgc@csps-efpc.gc.ca GC Data Community]</small></p>

<p><strong>[https://www.csps-efpc.gc.ca/catalogue/courses-eng.aspx?code=I511 The Role of Data in Digital Government]</strong> (virtual classroom)</p>

<p><strong>[https://www.csps-efpc.gc.ca/catalogue/courses-eng.aspx?code=I511 The Role of Data in Digital Government]</strong> (virtual classroom)</p>

<p class="recco">Recommended by the Canada School of Public Service, a GC Data Community partner</p>

<p class="recco">Recommended by the Canada School of Public Service, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/wtc/data-literacy Statistics Canada’s Data Literacy Training Initiative]</h3>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/wtc/data-literacy Statistics Canada’s Data Literacy Training Initiative]</h3>

<p>(In English) A re-cap of ODC’s Implementation Working Group meeting held last September 2021.</p>

<p>(In English) A re-cap of ODC’s Implementation Working Group meeting held last September 2021.</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/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>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/eng/data-science/network/automated-systems Responsible use of automated decision systems in the federal government]</h3>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/eng/data-science/network/automated-systems Responsible use of automated decision systems in the federal government]</h3>

<p class="author">Benoit Deshaies, Treasury Board of Canada Secretariat, and Dawn Hall, Treasury Board of Canada Secretariat</p>

<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Benoit_Deshaies Benoit Deshaies], Treasury Board of Canada Secretariat, and Dawn Hall, Treasury Board of Canada Secretariat</p>

<p>Data scientists play an important role in assessing data quality and building models to support automated decision systems. An understanding of when the Directive on Automated Decision-Making applies and how to meet its requirements can support the ethical and responsible use of these systems. In particular, the explanation requirement and the guidance (Guidance on Service and Digital, section 4.5.3.) from the Treasury Board of Canada Secretariat on model selection are of high relevance to data scientists.</p>

<p>Data scientists play an important role in assessing data quality and building models to support automated decision systems. An understanding of when the Directive on Automated Decision-Making applies and how to meet its requirements can support the ethical and responsible use of these systems. In particular, the explanation requirement and the guidance (Guidance on Service and Digital, section 4.5.3.) from the Treasury Board of Canada Secretariat on model selection are of high relevance to data scientists.</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.ineteconomics.org/perspectives/blog/hijacked-and-paying-the-price-why-ransomware-gangs-should-be-designated-as-terrorists Hijacked and Paying the Price - Why Ransomware Gangs Should be Designated as Terrorists]</h3>

<h3 style="text-decoration:none;">[https://www.ineteconomics.org/perspectives/blog/hijacked-and-paying-the-price-why-ransomware-gangs-should-be-designated-as-terrorists Hijacked and Paying the Price - Why Ransomware Gangs Should be Designated as Terrorists]</h3>

<p class="author">Melissa Hathaway</p>

<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Melissa_Hathaway Melissa Hathaway]</p>

<p>(In English) Ransomware gangs have been causing extensive damage. It’s time that the government takes them more seriously.</p>

<p>(In English) Ransomware gangs have been causing extensive damage. It’s time that the government takes them more seriously.</p>

<p class="recco">Recommended by the GC Data Community</p>

<p class="recco">Recommended by the GC Data Community</p>

<h3 style="text-decoration:none;">[https://www.cigionline.org/publications/patching-our-digital-future-unsustainable-and-dangerous/ Patching Our Digital Future Is Unsustainable and Dangerous]</h3>

<h3 style="text-decoration:none;">[https://www.cigionline.org/publications/patching-our-digital-future-unsustainable-and-dangerous/ Patching Our Digital Future Is Unsustainable and Dangerous]</h3>

<p class="author">Melissa Hathaway</p>

<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Melissa_Hathaway Melissa Hathaway]</p>

<p>(In English) In recent years, the world has witnessed an alarming number of high-profile cyber incidents, harmful information and communications technology (ICT) practices, and internationally wrongful acts through the misuse of ICTs. Over the last 30 years, a unique and strategic vulnerability has been brought to society — by allowing poorly coded or engineered, commercial-off-the-shelf products to permeate and power every aspect of our connected society. These products and services are prepackaged with exploitable weaknesses and have become the soft underbelly of government systems, critical infrastructures and services, as well as business and household operations. The resulting global cyber insecurity poses an increasing risk to public health, safety and prosperity. It is critical to become much more strategic about how new digital technologies are designed and deployed, and hold manufacturers of these technologies accountable for the digital security and safety of their products. The technology industry has fielded vulnerable products quickly — now, it is crucial to work together to reduce the risks created and heal our digital environment as fast as society can. This paper was first published as part of CIGI’s recent essay series, Governing Cyber Space during a Crisis in Trust.</p>

<p>(In English) In recent years, the world has witnessed an alarming number of high-profile cyber incidents, harmful information and communications technology (ICT) practices, and internationally wrongful acts through the misuse of ICTs. Over the last 30 years, a unique and strategic vulnerability has been brought to society — by allowing poorly coded or engineered, commercial-off-the-shelf products to permeate and power every aspect of our connected society. These products and services are prepackaged with exploitable weaknesses and have become the soft underbelly of government systems, critical infrastructures and services, as well as business and household operations. The resulting global cyber insecurity poses an increasing risk to public health, safety and prosperity. It is critical to become much more strategic about how new digital technologies are designed and deployed, and hold manufacturers of these technologies accountable for the digital security and safety of their products. The technology industry has fielded vulnerable products quickly — now, it is crucial to work together to reduce the risks created and heal our digital environment as fast as society can. This paper was first published as part of CIGI’s recent essay series, Governing Cyber Space during a Crisis in Trust.</p>

<p class="recco">Recommended by the GC Data Community</p>

<p class="recco">Recommended by the GC Data Community</p>

<h3 style="text-decoration:none;">[https://arxiv.org/abs/1811.10154 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead]</h3>

<h3 style="text-decoration:none;">[https://arxiv.org/abs/1811.10154 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead]</h3>

<p class="author">Cynthia Rudin</p>

<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Cynthia_Rudin Cynthia Rudin]</p>

<p>(In English) Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.</p>

<p>(In English) Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.</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>

<h2>Websites</h2>

<h2>Websites</h2>

<h3 style="text-decoration:none;">[https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?DGUID=2021A000011124 Census of Population]</h3>

<h3 style="text-decoration:none;">[https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?DGUID=2021A000011124 Census of Population]</h3>

<p>(In English) DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.</p>

<p>(In English) DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.</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 Data Science Centre]</h3>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science Data Science Centre]</h3>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network Data Science Network for the Federal Public Service]</h3>

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network Data Science Network for the Federal Public Service]</h3>

<p>Looking for a dynamic space to collaborate and learn about data science? Join the new Data Science Network for the Federal Public Service. Our vision is to create a vibrant community of data science enthusiasts and to offer a dynamic space for members to collaborate and learn about data science.</p>

<p>Looking for a dynamic space to collaborate and learn about data science? Join the new Data Science Network for the Federal Public Service. Our vision is to create a vibrant community of data science enthusiasts and to offer a dynamic space for members to collaborate and learn about data science.</p>

<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

<p>The Canadian Statistical Geospatial Explorer empowers users to discover Statistics Canada’s geo‑enabled data down to the smallest level of detail available, the dissemination area. Users can find, explore then export data in various formats to use in their workflows. Users can also customize the map and change basemaps (satellite imagery, topography, etc.) to view data in a different context.</p>

<p>The Canadian Statistical Geospatial Explorer empowers users to discover Statistics Canada’s geo‑enabled data down to the smallest level of detail available, the dissemination area. Users can find, explore then export data in various formats to use in their workflows. Users can also customize the map and change basemaps (satellite imagery, topography, etc.) to view data in a different context.</p>

<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

<!-- *** NEWSLETTERS + BLOGS *** -->

<!-- *** NEWSLETTERS + BLOGS *** -->

<h3 style="text-decoration:none;">[https://mailchi.mp/e0872fde637e/gc-data-community-mailing-list-sign-up-inscription-la-liste-de-diffusion-de-la-communaut-des-donnes-du-gc GC Data Community monthly newsletter]</h3>

<h3 style="text-decoration:none;">[https://mailchi.mp/e0872fde637e/gc-data-community-mailing-list-sign-up-inscription-la-liste-de-diffusion-de-la-communaut-des-donnes-du-gc GC Data Community monthly newsletter]</h3>

<p>Subscribe to keep up-to-date on data-related events, releases, jobs, and more throughout the Government of Canada.</p>

<p>Subscribe to keep up-to-date on data-related events, releases, jobs, and more throughout the Government of Canada.</p>

<br>

<p class="recco">Recommended by the GC Data Community</p>

<br>

<br>

<br>

<br>

<p>(In English) This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.</p>

<p>(In English) This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.</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>

<br>

<br>

<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>

<br>

<br>

[[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]]

[[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]]

<p>(In Engish) We live in a world of Big Data &#8212; and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it &#8212; 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 &#8212; 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>(In Engish) We live in a world of Big Data &#8212; and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it &#8212; 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 &#8212; 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>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<br>

<br>

<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>

<br>

<br>

<br>   

<br>   

<p>(In French - original title: <strong>Analyse des données textuelles</strong>) Textual data analysis (TDA) makes it possible to explore and visualize a wide range of text collections: literary works, interview transcripts, political speeches, press files, archival documents, online surveys with open-ended questions, complaint files, and satisfaction surveys. This book provides a rigorous presentation of TDA methods, which combine exploratory statistics, visualizations, quantitative validation procedures, and qualitative approaches.</p>

<p>(In French - original title: <strong>Analyse des données textuelles</strong>) Textual data analysis (TDA) makes it possible to explore and visualize a wide range of text collections: literary works, interview transcripts, political speeches, press files, archival documents, online surveys with open-ended questions, complaint files, and satisfaction surveys. This book provides a rigorous presentation of TDA methods, which combine exploratory statistics, visualizations, quantitative validation procedures, and qualitative approaches.</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>

<br>

<br>

<br>

<br>

<p>(In English) Quantitative bioimaging is a broad interdisciplinary field that exploits tools from biology, chemistry, optics, and statistical data analysis for the design and implementation of investigations of biological processes. Instead of adopting the traditional approach of focusing on just one of the component disciplines, this textbook provides a unique introduction to quantitative bioimaging that presents all of the disciplines in an integrated manner. The wide range of topics covered include basic concepts in molecular and cellular biology, relevant aspects of antibody technology, instrumentation and experimental design in fluorescence microscopy, introductory geometrical optics and diffraction theory, and parameter estimation and information theory for the analysis of stochastic data.</p>

<p>(In English) Quantitative bioimaging is a broad interdisciplinary field that exploits tools from biology, chemistry, optics, and statistical data analysis for the design and implementation of investigations of biological processes. Instead of adopting the traditional approach of focusing on just one of the component disciplines, this textbook provides a unique introduction to quantitative bioimaging that presents all of the disciplines in an integrated manner. The wide range of topics covered include basic concepts in molecular and cellular biology, relevant aspects of antibody technology, instrumentation and experimental design in fluorescence microscopy, introductory geometrical optics and diffraction theory, and parameter estimation and information theory for the analysis of stochastic data.</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>

<br>

<br>