Being, Training, and Employing Data Scientists

Canadian Statistical Sciences Institute

Wisdoms and Warnings from Harvard Data Science Review, presented by Founding Editor-in-Chief Xiao-Li Meng

This is a hybrid event. You may attend this lecture online or in person at Queen’s University in Kingston, Ontario.On March 21, 2024, the Department of Mathematics and Statistics and the Faculty of Arts and Science at Queen’s University in Kingston, Ontario, will host a distinguished lecture with Xiao-Li Meng, Founding Editor of Harvard Data Science Review and one of the world’s leading statisticians.The lecture will be preceded by a reception for in-person attendees. We hope you’ll be able to join us for this CANSSI-supported event! <h2>Schedule and Location</h2>The lecture will be held in Jeffery Hall 127, 48 University Avenue, Queen’s University, in Kingston, Ontario.The reception for in-person attendees will be held in front of Room 127 in Jeffery Hall. <ul><li>5:00–5:30 p.m. EDT | Reception (for in-person attendees)</li><li>5:30–6:30 p.m. EDT | Lecture</li></ul> <h2>Lecture Abstract</h2> <ul><li>“<a href="https://hdsr.mitpress.mit.edu/pub/5irjez4q/release/4?readingCollection=0699a97c" title="https://hdsr.mitpress.mit.edu/pub/5irjez4q/release/4?readingCollection=0699a97c" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">What Does It Take to Be a Successful Data Scientist?</a>”</li><li>“<a href="https://hdsr.mitpress.mit.edu/pub/gblp1ru7/release/2" title="https://hdsr.mitpress.mit.edu/pub/gblp1ru7/release/2" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Is Data Science Education a Jack of All Trades?</a>”</li><li>“<a href="https://hdsr.mitpress.mit.edu/pub/l469mo3u/release/2" title="https://hdsr.mitpress.mit.edu/pub/l469mo3u/release/2" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">How Can We Train Data Scientists When We Can’t Agree on Who They Are?</a>”</li></ul>These thought-provoking questions are the titles of articles in <a href="https://hdsr.mitpress.mit.edu/" title="https://hdsr.mitpress.mit.edu/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Harvard Data Science Review</a> (HDSR). This talk surveys and reflects on data science training, employment, and deployment in the BIG (Business, Industry, and Government) world based on such articles, and many more: <ul><li><a href="https://hdsr.mitpress.mit.edu/pub/0mb0zzlc/release/7?readingCollection=a133a0a2" title="https://hdsr.mitpress.mit.edu/pub/0mb0zzlc/release/7?readingCollection=a133a0a2" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">“Change Through Data: A Data Analytics Training Program for Government Employees”</a></li><li><a href="https://hdsr.mitpress.mit.edu/pub/t37qjoi7/release/4" title="https://hdsr.mitpress.mit.edu/pub/t37qjoi7/release/4" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">“Beyond Unicorns: Educating, Classifying, and Certifying Business Data Scientists”</a></li><li><a href="https://hdsr.mitpress.mit.edu/pub/6wx0qmkl/release/4?readingCollection=0699a97c" title="https://hdsr.mitpress.mit.edu/pub/6wx0qmkl/release/4?readingCollection=0699a97c" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">“Toward Foundations for Data Science and Analytics: A Knowledge Framework for Professional Standards”</a></li><li>“<a href="https://hdsr.mitpress.mit.edu/pub/60qq269p/release/1" title="https://hdsr.mitpress.mit.edu/pub/60qq269p/release/1" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">From Unicorn Data Scientist to Key Roles in Data Science: Standardizing Roles</a>”</li><li>“<a href="https://hdsr.mitpress.mit.edu/pub/2fu65ujf/release/6?readingCollection=0699a97c" title="https://hdsr.mitpress.mit.edu/pub/2fu65ujf/release/6?readingCollection=0699a97c" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Deployment as a Critical Business Data Science Discipline”</a></li><li><a href="https://hdsr.mitpress.mit.edu/pub/bfeyfx22/release/2?readingCollection=0699a97c" title="https://hdsr.mitpress.mit.edu/pub/bfeyfx22/release/2?readingCollection=0699a97c" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">“When Translation Problems Arise Between Data Scientists and Business Stakeholders, Revisit Your Metrics”</a></li><li><a href="https://hdsr.mitpress.mit.edu/pub/4vlrf0x2/release/2?readingCollection=0699a97c" title="https://hdsr.mitpress.mit.edu/pub/4vlrf0x2/release/2?readingCollection=0699a97c" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">”How to Define and Execute Your Data and AI Strategy”</a></li><li><a href="https://hdsr.mitpress.mit.edu/pub/a7gxkn0a/release/7?readingCollection=0699a97c" title="https://hdsr.mitpress.mit.edu/pub/a7gxkn0a/release/7?readingCollection=0699a97c" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">“A Balanced Perspective on Prediction and Inference for Data Science in Industry”</a></li><li><a href="https://hdsr.mitpress.mit.edu/pub/bj2dfcwg/release/3" title="https://hdsr.mitpress.mit.edu/pub/bj2dfcwg/release/3" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">“What Are the Values of Data, Data Science, or Data Scientists?</a>”</li><li><a href="https://doi.org/10.1162/99608f92.73fc83a3" title="https://doi.org/10.1162/99608f92.73fc83a3" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">To Deploy Machine Learning, You Must Manage Operational Change—Here Is How UPS Got It Right.</a></li></ul>

<h2>About Xiao-Li Meng</h2> Xiao-Li Meng, the Founding Editor-in-Chief of Harvard Data Science Review and the Whipple V. N. Jones Professor of Statistics at Harvard University, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by the Committee of Presidents of Statistical Societies (COPSS) in 2001, and he is the recipient of numerous awards and honours for his more than 150 publications. In 2020, he was elected to the American Academy of Arts and Sciences. Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004–2012) and the Dean of the Graduate School of Arts and Sciences (2012–2017).

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Being, Training, and Employing Data Scientists

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