Cecilia Aragonová se věnuje zkoumání hranic lidského potenciálu, od překonávání strachu po objevování nových forem učení. Její psaní často čerpá z jejích jedinečných zkušeností jako pilotky akrobatických letadel a jako první latinskoamerické profesorky inženýrství. Aragonová se zaměřuje na témata odvahy, mentorství a síly komunity, přičemž její styl je popisován jako pohlcující a inspirativní. Čtenáře zve na cestu, která je povznese a zároveň je přiměje k zamyšlení.
Flying Free is the story of how Cecilia Aragon broke free from expectations and rose above her own limits by combining her passion for flying with math and logic in unexpected ways
An in-depth examination of the novel ways young people support and learn from each other though participation in online fanfiction communities.
Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.