Communicating with Data

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 tolearn 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 IVaddresses 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, andparticipating 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.

This book about technical writing is particularly suitable (and necessary) for students in data science, or in any mathematical science. It begins with chapters on how to read technical articles, science news stories, and press releases ("examine the argument"); continues with chapters on how to describe and present data and code; advises how to get started writing ("who is the audience?"); and emphasizes the importance of editing and revising. A concluding chapter offers 22 imaginative exercises., excellent presentation.

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.