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数据分析技术丛书·深入解析SAS:数据处理、分析优化与商业应用

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数据分析技术丛书·深入解析SAS:数据处理、分析优化与商业应用

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SAS软件研究开发(北京)有限公司资深一线技术人员经验结晶,SAP大中国区商业创新首席架构师鲁百年强烈推荐。
实战性强,结合商业案例细致呈现SAS的优化建模方法,深入讲解SAS数据处理、统计分析及时间序列,涵盖引领大数据潮流的SAS高性能分析,以及智能分析平台、解决方案、平台的安全性与高可用性等重要领域。
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《数据分析技术丛书·深入解析SAS:数据处理、分析优化与商业应用》是国内市场目前一本由SAS公司在中国的员工创作、全面系统地剖析SAS技术的著作。作者团队结合自身实际工作的经验体会和大量生动的实践案例,通俗易懂、循序渐进地对SAS的核心技术模块和架构体系进行了全方位的介绍、总结与分享,帮助读者深刻领会和掌握使用SAS进行数据挖掘与优化的专业知识,同时培养读者运用这些专业知识解决商业问题和实施商业项目的能力。
全书共28章,分为四个部分:SAS编程(第1~8章)系统介绍了运用SAS进行数据读入、处理和展现等内容,掌握这一部分内容可以满足大部分实际项目中数据处理的需要;SAS统计分析和时间序列预测(第9~18章)全面介绍了多种常见统计方法的基本原理和利用SAS去实现的具体技术,包括描述性统计分析、参数估计与假设检验、方差分析、主成分分析与因子分析、聚类分析、判别分析、回归分析、时间序列分析等,并给出了相应的实践案例,从而让熟悉统计理论的读者有能力将其应用到实际中去;SAS优化建模(第19~24章)对于从事优化的读者来说,将是很好的帮助。这一部分主要介绍了运用SAS/OR建模,以及求解线性规划、混合整数规划问题的方法及实例,通过对常见的优化问题进行全面的阐述,帮助读者掌握优化思路和技巧;SAS商业应用(第25~28章)从项目实施角度探讨了如何设计满足安全性、高可用性和高性能需求的SAS应用,让读者领会解决实际问题的方法。
Author Description

夏坤庄,SAS软件研究开发(北京)有限公司客户职能部总监。在承担大量产品研发工作的同时,夏坤庄及其团队负责对SAS非英语市场提供技术支持,并且与在美国及其他地区的团队一起,对SAS的SaaS客户提供服务,以及提供和验证关于SAS产品和技术的很好实践。在加入SAS软件研究开发(北京)有限公司之前,夏坤庄就职于SAS中国公司。在SAS中国公司期间,历任资深咨询顾问、项目经理、首席顾问、咨询经理,在SAS的技术与产品领域拥有丰富的咨询和项目实施经验。在超过15年的从业经历中,为SAS的金融行业客户成功实施了众多深受好评的项目,所承担的项目获得诸如人民银行颁发的“银行系统科技进步一等奖”和客户系统内部颁发的“项目开发特等奖”等。拥有数学专业的学士学位和自动控制理论及应用专业的硕士学位。


徐唯,SAS软件研究开发(北京)有限公司资深分析咨询师,主要负责为SAS亚太地区客户提供高端商业分析与优化的咨询服务,拥有为国际客户提供数据挖掘和优化建模服务的丰富经验,例如为某大型国际汽车制造公司在中国业务的库存优化项目提供服务,以及为国际知名银行的信用卡审批流程优化提供优化建模服务等。本科和研究生均毕业于南京大学数学系。


潘红莲,SAS中国公司资深咨询顾问,为中国区客户提供SAS解决方案和产品的方案咨询和技术支持。于2008年加入SAS后,曾任SAS软件研究开发(北京)有限公司解决方案架构师,提供SAS解决方案和产品在企业级应用的方案研究和设计,尤其在IT实施和产品集成策略等领域做出了突出的贡献。对SAS架构设计、系统管理、安全性,以及高可用性等方面有着深刻的理解和丰富的实践经验。毕业于北京航空航天大学,获计算机专业学士和硕士学位。


林建伟,现就职于SAS软件研究开发(北京)有限公司,任资深分析咨询师。研究领域为大数据处理、数据挖掘、预测优化、库存优化、图论及相关应用。参与国内外多个客户的SAS预测与优化项目的咨询工作,例如为某海外银行的信用卡审批流程优化提供优化建模服务。美国西密歇根大学博士,在国际知名刊物上发表多篇专业论文。
Comments

★SAS引领大数据时代的发展,产品线覆盖了数据分析的各个方面,包括高性能分析、可视化分析和云计算等诸多新领域。但是从何处开始学习SAS、如何在实际工作中使用和部署SAS,却让很多初学者感到困惑,这时如果有长期使用SAS的专业人员把他们的宝贵经验分享出来,将会给大家更多的指导和帮助。而本书的几位作者恰好具有优秀的专业背景和丰富的SAS实践经验,其中,夏坤庄先生有5年多的时间在客户一线从事SAS产品咨询和项目实施工作;且在最近的9年中,他作为SAS研发北京中心客户职能部的总监,在进行产品研发的同时还在对SAS全球客户提供技术支持。而其他几位作者作为夏坤庄先生的同事,同样有着丰富的实践经验。本书的实战指导性非常强,它的结构体现了SAS核心内容的学习路线图,内容包括了SAS的核心技术模块乃至整体架构体系,并运用了很多源自实际应用的案例,非常适合广大数据分析从业者的需要。

— 刘政博士,SAS软件研究开发(北京)有限公司总经理


★我与本书作者之一夏坤庄先生在我行的一个大型项目中有着成功的合作。合作过程中,夏坤庄展现出了卓越的专业能力、项目把握能力及一丝不苟的敬业精神,以至于我团队中那些昔日的成员即使现在已身为分行或支行的行长,仍然会尊敬地回忆当年的夏工是如何手把手教导他们设计和开发的。现在,夏坤庄及其团队通过本书将他们丰富的知识和成功经验进行了总结和提炼,在对简单和优美的追求中,完成了对SAS系统的完整和深入介绍,满足了复杂的数据分析和大数据应用时代的要求。本书是不可错过的优美之作!

— 张晓波,中国工商银行软件开发中心上海研发支持部总经理、《计算机上的银行》作者


★大数据时代的核心是数据的分析和利用,分析利用的关键是模型和挖掘。本书不但通俗易懂地向大家介绍了数据分析的方法和应用案例,而且通过对 SAS高性能及SAS应用架构规划的解析,对如何将数据分析的方法和应用部署到大数据环境中,满足海量数据和大规模运算的要求做了清楚的介绍。是一本难得的好书!

—— 鲁百年博士,SAP大中国区商业创新首席架构师


★本书将统计分析和SAS应用成功地结合在了一起,既能够让熟悉统计理论的学习者有信心和能力将统计方法应用到现实中去,也能够让熟悉SAS的人有底气和理论高度在商业环境中选择和运用统计的分析方法。尤为难得的是,本书涵盖了预测及优化的丰富内容,并且结合复杂生动的案例对理论和方法进行了阐述。无论是对数据分析和SAS的初学者来说,还是对有不错基础的从业人员来说,本书都是很好的学习材料!
—— 赵彦云博士,中国人民大学统计学院院长、中国人民大学竞争力与评价研究中心主任
中国统计学会副会长
Catalogue

Part One (Chapter 1 - 8): SAS Programming and Data Processing
Chapter 1 Foundation of Base SAS
Chapter 2 Reading External Data to SAS Data Set
Chapter 3 SAS Data Set Processing
Chapter 4 Multiple Data Sets Processing
Chapter 5 Data Summary and Presentation
Chapter 6 SAS SQL Language
Chapter 7 SAS Macro Language
Chapter 8 SAS Programming with National Support

Part Two (Chapter 9 - 18): SAS Statistical Analysis and Time Series Forecasting
Chapter 9 Descriptive Statistical Analysis
Chapter 10 Parameter Estimation and Hypothesis Test
Chapter 11 Analysis of Variance
Chapter 12 Principal Component Analysis and Factor Analysis
Chapter 13 Cluster Analysis
Chapter 14 Discriminant Analysis
Chapter 15 Regression Analysis
Chapter 16 LOGISTIC Regression Analysis
Chapter 17 Time Series Analysis
Chapter 18 General process flow of SAS Data Mining

Part Three (Chapter 19 - 24): SAS Optimization Modeling
Chapter 19 Overview of Operational Research
Chapter 20 Fundamentals of Linear Programming
Chapter 21 Linear Programming with PROC OPTMODEL
Chapter 22 PROC OPTMODEL Programming
Chapter 23 Integer Linear Programming and Mixed Integer Linear Programming
Chapter 24 Examples of Optimization Modeling

Part Four (Chapter 25 - 28): SAS Business Implementation
Chapter 25 SAS Intelligence Platform and Solutions
Chapter 26 SAS Application Infrastructure Planning
Chapter 27 Security Administration of SAS Intelligence Platform
Chapter 28 High Availability of SAS Intelligence Platform
Introduction

为什么要写这本书
数据和模型描述着世界,而SAS恰恰就是关于数据和模型的技术。SAS技术在全球的数据处理和分析领域举足轻重。在国内,SAS的应用日趋广泛,自然,对掌握SAS技术的人才需求也日益旺盛。
但是当大家谈及SAS的时候,普遍的一个感受是,掌握SAS比较难。这使我记起在2000年刚刚加入SAS中国公司几天后的一个下午,时任SAS中国区技术总监的栾世武博士问我:“怎么样?SAS难学吗?”其实,在SAS公司的同事当中,大家并不会认为SAS有多难。究其原因,不过是如下几个:
在SAS公司,有着明确的路线图,大家可以清楚地知道学习SAS某个领域的顺序和步骤是什么。对于系统性非常强而且知识范围又较广的SAS而言,这是很重要的。
对于路线图中的每一个阶段,SAS公司都提供了详尽的资料供阅读和学习。
有实际的项目去实践和锻炼。
上面所提到的因素,也正是大部分期望学习SAS技术的从业者快速有效掌握SAS的“窍门”。基于这样的经历和思考,几年以来我一直在构思这样一本书:
1)以书中的章节结构来体现学习SAS核心内容的路线图。
2)在每个章节的内容中,包含路线图中对应部分的必要学习资料,并且使得读者在读完相应的内容之后,有能力并且了解如何去学习更加深入和广泛的知识。
3)提供贴近实际应用项目甚至有些复杂的例子,让读者领会解决实际问题的思路和技巧。
本书就是基于上述构思的一个实现,希望能够帮助大家系统地掌握SAS的专业知识,进而从容地将其应用于商业实际中。
读者对象
本书主要适合于以下读者:
使用SAS进行数据抽取、转换和清洗的技术人员。
需要使用SAS对数据进行深入分析和数据挖掘的分析人员。
需要使用SAS进行时间序列预测和优化决策的建模专家。
使用SAS进行项目规划、实施和管理的系统架构师、系统管理员和项目管理人员。
团队的工作涉及SAS产品与技术的管理人员。
如何阅读本书
本书共4篇,系统介绍了SAS的核心技术模块和架构体系。
第一篇介绍SAS编程和数据处理(第1~8章)。内容包括如何运用SAS进行数据读入、处理和展现。掌握本书的这一篇内容可以满足大部分实际项目中数据处理的需要。该篇建议刚刚接触SAS的读者仔细研读,对SAS编程有全面了解的读者可以快速浏览或者在需要时查阅。
第二篇介绍SAS统计分析和时间序列预测(第9~18章)。内容既包括基本的理论介绍,又包括如何利用SAS去实现的具体技术。该篇建议需要学习数据分析、数据挖掘或进行预测的读者仔细阅读。
第三篇介绍SAS优化建模(第19~24章)。对于从事优化的读者来说,这一篇的内容将很有帮助。这一篇对常见的优化问题做了全面的介绍。其中的用例非常贴近现实,建议读者仔细研读。此外,建议从事优化的读者也学习一下第二篇中第17章关于时间序列分析的内容,因为在实际优化工作中,经常需要预测。
第四篇介绍SAS智能平台架构体系(第25~28章)。对于该篇内容,不需要像前3篇一样有深入的掌握,但这些内容对于项目规划和架构设计人员设计一个满足安全性、高可用性和高性能的SAS应用会非常有帮助。
致谢
本书的完成是整个写作团队合作的成果,蕴含着每一个作者的努力。
在本书即将完成之际,需要感谢的名字很多,把这长长的列表沉在深处之后,在此感谢我们所处的时代和我所在的SAS公司。时代赋予了企业和个人对数据进行分析和建模的需求,SAS公司给予了我们完成本书所需要的知识和使命感。
特别感谢机械工业出版社华章公司的Lisa Yang。感谢Lisa的热情相邀和宝贵建议,促成了本书的完成,她的专业而高效的审阅,也使得本书增色极多。
夏坤庄(Kansun Xia)
北京,2014年7月


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Jingdong book

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About the brand

Jingdong book