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Data Conference 2022 Speakers (view source)
Revision as of 13:17, 2 February 2022
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<p>Participating in <strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda#Disaggregated_data:_Equity.2C_diversity_and_inclusion Disaggregated data: Equity, diversity, and inclusion]</strong></p>
<p>Participating in <strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda#Disaggregated_data:_Equity.2C_diversity_and_inclusion Disaggregated data: Equity, diversity, and inclusion]</strong></p>
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[[Image:Eric Rancourt.jpg|150px|Eric Rancourt]]
<h3>Eric Rancourt</h3>
<p class="jobtitle">Director General, Modern Statistical Methods and Data Science Branch, Statistics Canada</p>
<p>Eric Rancourt is Director General of the Modern Statistical Methods and Data Science Branch and Principal Data Ethics and Scientific Integrity Officer at Statistics Canada. He has worked at Statistics Canada for 31 years and has occupied several roles such as Director General of Strategic Data Management, Director of International Cooperation, Director of Corporate Planning, Head of research, Production manager of Survey Methodology Journal, and Researcher. His main areas of work have been on treatment of missing data, estimation, gathering, safeguarding and using of administrative and alternative data in statistical programs. Recently his work has been on frameworks for optimizing privacy and information and on data ethics. He holds a BSc in Statistics from University Laval as well as a BA in Ancient Studies and in Medieval Studies from the University of Ottawa. He is currently completing studies in philosophy, focusing on data ethics. He is Chair of the Canadian Statistical Sciences Institute (CANSSI). He has been involved in many professional statistical associations and is an elected member of the International Statistical Institute ISI. His recent papers are on admin data as a paradigm, the application of the scientific approach and the use of alternative data collection methods. </p>
<p>Participating in <strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda#Privacy_Frameworks_and_data_for_public_good Privacy Frameworks and data for public good]</strong></p>
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