Issue 6.1, Winter 2024

From the Editor-in-Chief

2024: A Year of Crises, Change, Contemplation, and Commemoration

by Xiao-Li Meng

Issue 6.1 / Winter 2024

Panorama

Overviews, Visions, and Debates

Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

by Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, and 16 more

Featured Discussion

Data Science at the Singularity

by David Donoho

by Lorena Barba

The Path to Frictionless Reproducibility Is Still Under Construction

by Juliana Freire

The Singularity in Data and Computation-Driven Science: Can It Scale Beyond Machine Learning?

by Matan Gavish

A Familiar, Invisible Engine Is Driving the AI Revolution

by Andrew Gelman

Hopes and Limitations of Reproducible Statistics and Machine Learning

by Peyman Milanfar

Data Science at the Precipice

by Benjamin Recht

The Mechanics of Frictionless Reproducibility

by Peter Rousseeuw

An Alternate History for Machine Learning?

by Stephen Ruberg

Analytics Challenges and Their Challenges

by Joseph Salmon

Collective Intelligence and Collaborative Data Science

by Adam D. Schuyler

Overcoming Potential Obstacles as We Strive for Frictionless Reproducibility

by Terrence J. Sejnowski

Data Science Is an Ecosystem

by Victoria Stodden

On Emergent Limits to Knowledge—Or, How to Trust the Robot Researchers: A Pocket Guide

by Keyon Vafa

Is Causal Inference Compatible With Frictionless Reproducibility?

by Brian Wandell

Metadata, Instrumentation, and Netsplaining

by Bin Yu

After Computational Reproducibility: Scientific Reproducibility and Trustworthy AI

by Zhiwei Zhu

Rethinking the Data Science Singularity

Rejoinder to Discussion of "Data Science at the Singularity"

by David Donoho

Cornucopia

Impact, Innovation, and Knowledge Transfer

Assessing the Prognostic Utility of Clinical and Radiomic Features for COVID-19 Patients Admitted to ICU: Challenges and Lessons Learned

by Yuming Sun, Stephen Salerno, Ziyang Pan, Eileen Yang, Chinakorn Sujimongkol, Jiyeon Song, Xinan Wang, Peisong Han, and 4 more

Stepping Stones

Learning, Teaching, and Communication

What Should Data Science Education Do With Large Language Models?

by Xinming Tu, James Zou, Weijie Su, and Linjun Zhang

Milestones and Millstones

Foundations, Theories, and Methods

Causation, Comparison, and Regression

by Ambarish Chattopadhyay and José R. Zubizarreta

Catalytic Causal Conversations

Causal Inferences From Diverse Perspectives

Column Co-Editors: Iavor Bojinov and Francesca Dominici

Causal Inference for Everyone

by Iavor Bojinov and Francesca Dominici

Diving into Data

Mini Tutorials on Concepts, Methods, and Tools

Column Editor: Sach Mukherjee

When Data Science Goes Wrong: How Misconceptions About Data Capture and Processing Causes Wrong Conclusions

by Peter Christen and Rainer Schnell

Effective Policy Learning

Data Science for Policy Making and Makers

Column Co-Editors: Nancy Potok and Nick Dudley Ward

The Groundwater Crisis: The Need for New Data to Inform Public Policy

by Nick Dudley Ward

Minding the Future

Building Pipelines for Data Science

Column Editor: Nicole Lazar

Teaching Introductory Statistics in a World Beyond “p < .05”

by Catherine Case

Mining the Past

Brief Histories of Data Science

Column Co-Editors: Stephanie Dick and Christopher J. Phillips

Discerning Audiences Through Like Buttons

by Carina Albrecht

Reinforcing Reproducibility and Replicability

A Path to Better (Data) Science

Column Editor: Lars Vilhuber

Why and How We Share Reproducible Research at Yale University’s Institution for Social and Policy Studies

by Limor Peer