莫言，山东高密人，1955年生，2012年诺贝尔文学奖获得者。著有《红高粱家族》、《酒国》、《丰乳肥臀》、《檀香刑》、《生死疲劳》、《蛙》等长篇小说十一部，《透明的红萝卜》、《白狗秋千架》、《师傅越来越幽默》等中短篇小说一百余部，并著有剧作、散文多部；其中多部作品已被译为英、法、德、意、日、西、俄、韩、荷兰、瑞典、挪威、波兰、阿拉伯、越南等多种语言，在国内外文坛上具有广泛影响。 莫言及其作品曾获：“华语文学传媒大奖•年度杰出成就奖”、“茅盾文学奖”，香港浸会大学“世界华文长篇小说奖•红楼梦奖”，台湾“联合文学奖”，法国“Laure Bataillon（儒尔•巴泰庸）外国文学奖”、“法兰西文化艺术骑士勋章”，意大利“Nonino（诺尼诺）国际文学奖”，日本“福冈亚洲文化大奖”，美国“纽曼华语文学奖”，等。

莫言，山东高密人，1955年生，2012年诺贝尔文学奖获得者。著有《红高粱家族》、《酒国》、《丰乳肥臀》、《檀香刑》、《生死疲劳》、《蛙》等长篇小说十一部，《透明的红萝卜》、《白狗秋千架》、《师傅越来越幽默》等中短篇小说一百余部，并著有剧作、散文多部；其中多部作品已被译为英、法、德、意、日、西、俄、韩、荷兰、瑞典、挪威、波兰、阿拉伯、越南等多种语言，在国内外文坛上具有广泛影响。 莫言及其作品曾获：“华语文学传媒大奖•年度杰出成就奖”、“茅盾文学奖”，香港浸会大学“世界华文长篇小说奖•红楼梦奖”，台湾“联合文学奖”，法国“Laure Bataillon（儒尔•巴泰庸）外国文学奖”、“法兰西文化艺术骑士勋章”，意大利“Nonino（诺尼诺）国际文学奖”，日本“福冈亚洲文化大奖”，美国“纽曼华语文学奖”，等。

维克托·迈尔—舍恩伯格（Viktor Mayer-Schönberger）牛津大学互联网研究所教授，担任微软、世界经济论坛等大公司和组织的顾问，是大数据领域公认的权威，写过《大数据时代》等八本书和上百篇论文。 肯尼思·库克耶（Kenneth Cukier） 《经济学人》（The Economist）数据编辑，是一位著名的大数据发展评论员。 赵中建 华东师范大学课程与教学研究所教授，上海纽约大学文理学院副院长，国家基础教育课程教材专家工作委员会委员，长期致力于国际教育政策和基础教育研究。 张燕南 上海第二军医大学教师、华东师范大学博士。

This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows.It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.

This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies.

Big Mistakes: The Best Investors and Their Worst Investments explores the ways in which the biggest names have failed, and reveals the lessons learned that shaped more successful strategies going forward. Investing can be a rollercoaster of highs and lows, and the investors detailed here show just how low it can go; stories from Warren Buffet, Bill Ackman, Chris Sacca, Jack Bog...(展开全部)

This text seeks to demonstrate that combining a sound understanding of economic and managerial principles can make a striking difference in the quality of the strategic planning of an organization and provide guidelines for effective corporate strategies. This third edition includes new material in game theory, added value analysis and strategic intent. Examples are drawn from ...(展开全部)

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.