Showing results for "Computer Science Engineering" in Data Science

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, this book presents ninety-seven concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Neste livro que já se tornou um clássico, David Spiegelhalter — um dos maiores estatísticos da atualidade — combina a visão incomparável de um especialista com o entusiasmo de um aficionado e oferece um verdadeiro guia para explorarmos a arte dessa ciência que é muito mais do que um ramo da matemática. Em A arte da estatística, David Spiegelhalter utiliza a resolução de questões do mundo real como ponto de partida para introduzir conceitos estatísticos, dos simples aos mais complexos, sempre com notório didatismo.