Domino Data Science Blog

Ann Spencer

Ann Spencer is the former Head of Content for Domino where she provided a high degree of value, density, and analytical rigor that sparks respectful candid public discourse from multiple perspectives, discourse that’s anchored in the intention of helping accelerate data science work. Previously, she was the data editor at O’Reilly, focusing on data science and data engineering.

Data Science

Model Interpretability: The Conversation Continues

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from Bin Yu, W. James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi's recent paper, "Definitions, methods, and applications in interpretable machine learning".

By Ann Spencer9 min read

Perspective

On Being Model-driven: Metrics and Monitoring

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and performance in the future. Many thanks to Don Miner for collaborating with Domino on this article. For additional vital signs and insight beyond what is provided in this article, attend the webinar.

By Ann Spencer7 min read

Perspective

Data Ethics: Contesting Truth and Rearranging Power

This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing for ethics and for understanding the forces that encourage industry to operationalize ethics, as well as 2) proposed ethical principles for data scientists to consider when developing data-empowered products. Many thanks to Chris for providing feedback on this post prior to publication and for the permission to excerpt his slides.

By Ann Spencer12 min read

Data Science

Data Science, Past & Future

Paco Nathan presented, "Data Science, Past & Future", at Rev. This blog post provides a concise session summary, a video, and a written transcript.

By Ann Spencer56 min read

Perspective

Seeking Reproducibility within Social Science: Search and Discovery

Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search & Discovery” at Rev. Lane described the approach that the Coleridge Initiative is taking to address the science reproducibility challenge. The approach is to provide remote access for government analysts and researchers to confidential data in a secure data facility and to build analytical capacity and collaborations through an Applied Data Analytics training program. This article provides a distilled summary and a written transcript of Lane’s talk at Rev. Many thanks to Julia Lane for providing feedback on this post prior to publication.

By Ann Spencer25 min read

Perspective

Data Science at The New York Times

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. Many thanks to Chris Wiggins for providing feedback on this post prior to publication.

By Ann Spencer40 min read

Subscribe to the Domino Newsletter

Receive data science tips and tutorials from leading Data Science leaders, right to your inbox.

*

By submitting this form you agree to receive communications from Domino related to products and services in accordance with Domino's privacy policy and may opt-out at anytime.