Communicating with Data The Art of Writing for Data Science

Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component.

Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.

Deborah Nolan, Professor, University of California,Sara Stoudt

Deborah Nolan is Professor of Statistics and Associate Dean for Undergraduate Education in the Division of Computing, Data Science, and Society at the University of California, Berkeley, where she holds the Zaffaroni Family Chair in Undergraduate Education. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research.


Sara Stoudt is an applied statistician whose work primarily focuses on environmental and ecological applications. She is currently a lecturer at Smith College. She received her PhD in Statistics from UC Berkeley. Prior to that she received a B.A. in Mathematics (with an emphasis on statistics) from Smith College.