文件名称：Computing with Data: An Introduction to the Data Industry
文件大小：7.31MB
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更新时间：2022-02-17 13:44:35
Python
Computing with Data: An Introduction to the Data Industry By 作者: Guy Lebanon – Mohamed El-Geish ISBN-10 书号: 331998148X ISBN-13 书号: 9783319981482 Edition 版本: 1st ed. 2018 出版日期: 2018-11-29 pages 页数: (576 ) Springer出版超清 This book introduces basic computing skills designed for industry professionals without a strong computer science background. Written in an easily accessible manner, and accompanied by a user-friendly website, it serves as a self-study guide to survey data science and data engineering for those who aspire to start a computing career, or expand on their current roles, in areas such as applied statistics, big data, machine learning, data mining, and informatics. The authors draw from their combined experience working at software and social network companies, on big data products at several major online retailers, as well as their experience building big data systems for an AI startup. Spanning from the basic inner workings of a computer to advanced data manipulation techniques, this book opens doors for readers to quickly explore and enhance their computing knowledge. Computing with Data comprises a wide range of computational topics essential for data scientists, analysts, and engineers, providing them with the necessary tools to be successful in any role that involves computing with data. The introduction is self-contained, and chapters progress from basic hardware concepts to operating systems, programming languages, graphing and processing data, testing and programming tools, big data frameworks, and cloud computing. The book is fashioned with several audiences in mind. Readers without a strong educational background in CS–or those who need a refresher–will find the chapters on hardware, operating systems, and programming languages particularly useful. Readers with a strong educational background in CS, but without significant industry background, will find the following chapters especially beneficial: learning R, testing, programming, visualizing and processing data in Python and R, system design for big data, data stores, and software craftsmanship. Cover Front Matter 1.Introduction:How to Use This Book? 2.Essential Knowledge:Hardware 3.Essential nowledge:Operating Systems 4.Learning C++ 5.Learning Java 6.Learning Python and a Few lMore Things 7.Learning R 8.Visualizing Data in R and Python 9.Processing Data in R and Python 10.Essential knowledge:Parallel Programming 11.Essential Knowledge:Testing 12.A Few More Things About Proramming 13.Essential nowledge:Data Stores 14.Thoughts on System Desin for Big Data 15.Thoughts on Software Craftsmanship