Issue 3.1, Winter 2021

by Margaret V. Powers-Fletcher, Erin E. McCabe, Sally Luken, Danny Wu, Philip A. Hagedorn, Ezra Edgerton, Amy Koshoffer, Dorcas Washington, and 5 more

Stepping Stones

Learning, Teaching, and Communication

Featured Discussion

Statistics Practicum: Placing 'Practice' at the Center of Data Science Education

by Eric D. Kolaczyk, Haviland Wright, and Masanao Yajima

by Shuang Frost, Aleksandrina Goeva, Javin Pombra, William Seaton, Sara Stoudt, Ana Trisovic, Chris Wang, and Catherine Zucker

Kaleidoscopic Perspectives on Practicum-Based Data Science Education

An interview with Shuang Frost, Aleksandrina Goeva, Javin Pombra, Sara Stoudt, Ana Trisovic, and Chris Wang by William Seaton and Catherine Zucker

by Kit T. Rodolfa and Rayid Ghani

Taking Our Medicine: Standardizing Data Science Education With Practice at the Core

by Usama Fayyad and Hamit Hamutcu

How Can We Train Data Scientists When We Can’t Agree on Who They Are?

by David R. Hardoon

Is Data Science Education a Jack of All Trades?

by Nicholas Jon Horton, Benjamin S. Baumer, Andrew Zieffler, and Valerie Barr

The Data Science Corps Wrangle-Analyze-Visualize Program: Building Data Acumen for Undergraduate Students

by Philip L. H. Yu and Wai Keung Li

Project-Based Learning via Competition for Data Science Students

by Antonietta Mira and Ernst Wit

The Capstone in Everyone’s Delivery Room: Placing ‘Practice’ at the Center of Data Science Education

by David Uminsky

From Outlier to Normalcy: Community Partnering as a Pathway Toward Modularity, Sustainability and Diversity When Centering ‘Practice’ in Data Science Education

by Jessica Utts

Statistical Practice Is Not a Spectator Sport

Rejoinder: Practice-Centric Learning—A Driver for Democratization of Data Science

by Eric D. Kolaczyk, Haviland Wright, and Masanao Yajima

Milestones and Millstones

Foundations, Theories, and Methods

Recombination: A Family of Markov Chains for Redistricting

by Daryl DeFord, Moon Duchin, and Justin Solomon

Active Industrial Learning

Data Science for Industrial Leaders

Column Co-Editors: Tom Davenport and Katie Malone

Deployment as a Critical Business Data Science Discipline

by Thomas Davenport and Katie Malone

Effective Policy Learning

Data Science for Policy Making and Makers

Column Co-Editors: Frauke Kreuter and Nancy Potok

Understanding the Quality of the 2020 Census Is Essential

by John Thompson

Mining the Past

Brief Histories of Data Science

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

An Interview With Murray Edelman on the History of the Exit Poll

by Murray Edelman, Liberty Vittert, and Xiao-Li Meng

An interview with Murray Edelman by Liberty Vittert and Xiao-Li Meng

Recreations in Randomness

Data Science for Leisure Activities

Column Editor: Mark Glickman

Why Tennis Is Still Not Ready to Play Moneyball

by Stephanie Kovalchik

What Are the Values of Data, Data Science, or Data Scientists?

Data Science in Times of Pan(dem)ic

Data Scientists Should Be Value-Driven, Not Neutral

We Need a (Responsible!) Data Science Rapid Response Network

The Arc of the Data Scientific Universe

Bringing Every Tool to the COVID-19 Fight—What We Need Now

More Efficient and Effective Clinical Decision-Making

A Plague of Data

Blue Chips and White Collars: Whose Data Science Is It?

Rejoinder: The Present and Future of Data Science in Society

The People Machine: The Earliest Machine Learning?

Combining Statistical, Physical, and Historical Evidence to Improve Historical Sea-Surface Temperature Records

Convergence in Viral Outbreak Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm