Writing a data book: Panel discussion

Have you ever wanted to write a book about a data or software topic? Just want to hang out with fun people, come join us at at the Data Science DC panel discussion on writing a data book.

Logistics
This event will be in person. We will try to add an online streaming and recording option if the space, WIFI, and hardware will accommodate, starting at 7pm. Streaming and recording will be on the Data Community DC YouTube channel: youtube.com/@DataCommunityDC

Agenda
6:30pm - Food and networking
7:00pm - Talk time
After the talk, some folks will likely head across the street to Courthaus Social

Panelists
Lauren Maffeo is an award-winning author, analyst, and designer of data systems for U.S. state and federal governments. Her first book, Designing Data Governance from the Ground Up, was published by The Pragmatic Programmers and adapted into a LinkedIn Learning course, with a second course to follow this autumn. Lauren is an Assistant Director of Product on the founding digital services team for the State of Maryland's Family and Medical Leave Insurance (FAMLI), where her team is building the first platform to provide paid caregiver leave in Maryland. She is also an adjunct lecturer of Interaction Design at The George Washington University

Peter Bruce is the Founder of the Institute for Statistics Education at Statistics.com. Founded in 2002, Statistics.com was an early pioneer in online education and the first educational institution devoted solely to online education in Statistics and Data Science. He is also a co-author of Statistics for Data Science and Analytics (Wiley), Machine Learning for Business Analytics (Wiley), Responsible Data Science (Wiley), Practical Statistics for Data Science (O'Reilly), and the author of Introductory Statistics and Analytics: A Resampling Perspective. His books have appeared in 6 languages, in addition to English. Prior to his career in statistics, Peter served in the U.S. Foreign Service.

Peter Gedeck is a data scientist at Collaborative Drug Discovery, where he focuses on the development of novel cheminformatics approaches for drug discovery. He also teaches statistical learning in the School of Data Science at the University of Virginia. In addition to over 50 peer reviewed articles, he co-authored six books. The most popular one is “Practical Statistics for Data Scientists” (O’Reilly) which was translated into eight other languages. The other books are textbooks on machine learning for business analytics and statistics.