描述
开 本: 16开纸 张: 胶版纸包 装: 平装-胶订是否套装: 否国际标准书号ISBN: 9787302505723丛书名: 大数据技术与应用专业规划教材
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《大数据专业英语教程》所选取的文章包括以下内容：大数据的基本概念，大数据的数据挖掘，大数据的数据分析，大数据的影响，大数据的商业价值，大数据在各个领域的应用，以及大数据如何改变我们的生活等。每章所选用文章均来自国外网站，并对文章出现的新词和专业术语进行了注释，每篇文章配有相应的习题和拓展阅读，以巩固学习效果。
由于大数据专业处于高速发展之中，国际化特征尤为明显，从业人员必须提高专业英语水平，以便及时获得1新、1先进的专业知识。从某种意义上说，专业英语的水平决定了专业技能的水平。因此，几乎所有开设大数据专业的高校都开设了相应的专业英语课程。面对广大的从业人员，为了紧跟大数据技术的前沿，了解和掌握一些大数据专业英语是非常有必要的。《大数据专业英语教程》可作为大数据专业公共必修课教材，英语专业及计算机专业的选修教材，也可作为各类院校大数据和相关专业教学用书，同时作为各类计算机从业人员或者有志投身于150万人才缺口大数据领域的人士之自学书籍。
《大数据专业英语教程》的内容包括大数据的基本概念，大数据的数据挖掘，大数据的数据分析，大数据的影响，大数据的商业价值，大数据在各个领域的应用，以及大数据如何改变我们的生活等。每个单元包括：Text A及Text B两篇文章，这些课文均选自国外知名网站，具有一定的知识性和实用性；New Words and Expressions给出课文中出现的新词，读者由此可以扩充词汇量；Terms对文中出现的专业术语进行解释； Comprehension针对课文练习，巩固学习效果；Answers给出参考答案，读者可对照检查学习效果；参考译文帮助读者理解文章大意；常用大数据词汇中英文对照表供读者记忆单词和查询之用。
由于大数据专业处于高速发展之中，国际化特征尤为明显，从业人员必须提高专业英语水平，以便及时获得1新、1先进的专业知识。从某种意义上说，专业英语的水平决定了专业技能的水平。因此，几乎所有开设大数据专业的高校都开设了相应的专业英语课程。面对广大的从业人员，为了紧跟大数据技术的前沿，了解和掌握一些大数据专业英语是非常有必要的。《大数据专业英语教程》可作为大数据专业公共必修课教材，英语专业及计算机专业的选修教材，也可作为各类院校大数据和相关专业教学用书，同时作为各类计算机从业人员或者有志投身于150万人才缺口大数据领域的人士之自学书籍。
《大数据专业英语教程》的内容包括大数据的基本概念，大数据的数据挖掘，大数据的数据分析，大数据的影响，大数据的商业价值，大数据在各个领域的应用，以及大数据如何改变我们的生活等。每个单元包括：Text A及Text B两篇文章，这些课文均选自国外知名网站，具有一定的知识性和实用性；New Words and Expressions给出课文中出现的新词，读者由此可以扩充词汇量；Terms对文中出现的专业术语进行解释； Comprehension针对课文练习，巩固学习效果；Answers给出参考答案，读者可对照检查学习效果；参考译文帮助读者理解文章大意；常用大数据词汇中英文对照表供读者记忆单词和查询之用。
内容简介
本书是计算机、信息管理和大数据等相关专业的专业英语教材，选材广泛，覆盖大数据的数据挖掘、数据分析等各个方面，同时兼顾了相关的发展热点。本书所选取的文章包括以下内容：大数据的基本概念，大数据的数据挖掘，大数据的数据分析，大数据的影响，大数据的商业价值，大数据在各个领域的应用，以及大数据如何改变我们的生活等。每章所选用文章均来自国外网站，文章中出现的新词和专业术语也均有注释，每篇文章配有相应的习题和拓展阅读，以巩固学习效果。
目 录
Chapter 1 What is Big Data? 1
Text A 1
Comprehension 4
Answers 5
参考译文 6
Text B 8
参考译文 9
Chapter 2 Data Mining For Big Data 11
Text A 11
Terms 13
Comprehension 15
Answers 16
参考译文 16
Text B 17
参考译文 19
Chapter 3 Big Data Analytics 22
Text A 22
Terms 24
Comprehension 30
Answers 31
参考译文 32
Text B 33
Terms 36
Comprehension 40
Answers 40
参考译文 41
Chapter 4 Impacts of Big Data 43
Text A 43
Terms 46
Comprehension 50
Answers 51
参考译文 51
Chapter 5 Business Benefits of Big Data 53
Text A 53
Terms 58
Comprehension 62
Answers 62
参考译文 63
Chapter 6 Application of Big Data 66
Text A 66
Terms 70
Comprehension 70
Answers 71
参考译文 71
Text B 73
参考译文 74
Chapter 7 Big Data in Recruitment Marketing 76
Text A 76
Terms 79
Comprehension 80
Answers 80
参考译文 81
Text B 82
参考译文 86
Chapter 8 Big Data in Gaming Industries 89
Text A 89
Comprehension 91
Answers 92
参考译文 93
Text B 94
参考译文 96
Chapter 9 Big Data in Education 98
Text A 98
Comprehension 100
Answers 101
参考译文 102
Text B 103
参考译文 106
Chapter 10 Big Data in Health 109
Text A 109
Comprehension 112
Answers 112
参考译文 113
Text B 114
参考译文 116
Chapter 11 Big Data in Banking 118
Text A 118
Comprehension 121
Answers 121
参考译文 122
Text B 123
参考译文 127
附录A 常用大数据词汇中英文对照表 130
附录B 存储容量单位换算 138
参考文献 139
Text A 1
Comprehension 4
Answers 5
参考译文 6
Text B 8
参考译文 9
Chapter 2 Data Mining For Big Data 11
Text A 11
Terms 13
Comprehension 15
Answers 16
参考译文 16
Text B 17
参考译文 19
Chapter 3 Big Data Analytics 22
Text A 22
Terms 24
Comprehension 30
Answers 31
参考译文 32
Text B 33
Terms 36
Comprehension 40
Answers 40
参考译文 41
Chapter 4 Impacts of Big Data 43
Text A 43
Terms 46
Comprehension 50
Answers 51
参考译文 51
Chapter 5 Business Benefits of Big Data 53
Text A 53
Terms 58
Comprehension 62
Answers 62
参考译文 63
Chapter 6 Application of Big Data 66
Text A 66
Terms 70
Comprehension 70
Answers 71
参考译文 71
Text B 73
参考译文 74
Chapter 7 Big Data in Recruitment Marketing 76
Text A 76
Terms 79
Comprehension 80
Answers 80
参考译文 81
Text B 82
参考译文 86
Chapter 8 Big Data in Gaming Industries 89
Text A 89
Comprehension 91
Answers 92
参考译文 93
Text B 94
参考译文 96
Chapter 9 Big Data in Education 98
Text A 98
Comprehension 100
Answers 101
参考译文 102
Text B 103
参考译文 106
Chapter 10 Big Data in Health 109
Text A 109
Comprehension 112
Answers 112
参考译文 113
Text B 114
参考译文 116
Chapter 11 Big Data in Banking 118
Text A 118
Comprehension 121
Answers 121
参考译文 122
Text B 123
参考译文 127
附录A 常用大数据词汇中英文对照表 130
附录B 存储容量单位换算 138
参考文献 139
前 言
随着时代的进步和社会的高速发展，互联网行业发展风起云涌，移动互联网、电子商务、物联网以及社交媒体的快速发展促使我们快速进入了大数据时代。大数据技术与应用相关专业前景相当广阔，大数据人才需求旺盛，2017年我国已有35所高校获批该专业。
大数据专业处于高速发展之中，国际化特征尤为明显，从业人员必须提高专业英语水平，以便及时获得最新、最先进的专业知识。从某种意义上说，专业英语的水平决定了专业技能的水平。了解和掌握一些大数据专业英语是非常有必要的，因此，几乎所有开设大数据专业的高校都开设了相应的专业英语课程。
本书的内容包括：大数据的基本概念，大数据的数据挖掘，大数据的数据分析，大数据的影响，大数据的商业价值，大数据在各个领域的应用，以及大数据如何改变我们的生活等。每个单元基本上包括：Text A及Text B两篇文章，这些课文均选自国外知名网站，具有一定的知识性和实用性；New Words and Expressions给出课文中出现的新词，读者由此可以扩充词汇量；Terms对文中出现的专业术语进行解释；Comprehension针对课文进行练习，巩固学习效果；Answers给出参考答案，读者可对照检查学习效果；参考译文帮助读者理解文章大意；常用大数据词汇中英文对照表供读者记忆单词和查询之用。
本书可作为大数据专业相关课程教材，英语专业及计算机专业的选修教材，各类院校大数据和相关专业的参考书，同时也可作为各类计算机从业人员或者有志投身于大数据领域的人士的自学书籍。
本书第1章（Chapter 1）至第8章（Chapter 8）由朱丹编写，第9章（Chapter 9）及常用大数据词汇中英文对照表由王敏编写，第10章（Chapter 10）由张琦编写，第11章（Chapter 11）由陈宏编写。全书由朱丹统稿。
本书文章节选自互联网，在此向文章原作者表示感谢，由于作者水平有限，书中难免出现不足之处，敬请读者不吝指正。
编者
2018年6月
大数据专业处于高速发展之中，国际化特征尤为明显，从业人员必须提高专业英语水平，以便及时获得最新、最先进的专业知识。从某种意义上说，专业英语的水平决定了专业技能的水平。了解和掌握一些大数据专业英语是非常有必要的，因此，几乎所有开设大数据专业的高校都开设了相应的专业英语课程。
本书的内容包括：大数据的基本概念，大数据的数据挖掘，大数据的数据分析，大数据的影响，大数据的商业价值，大数据在各个领域的应用，以及大数据如何改变我们的生活等。每个单元基本上包括：Text A及Text B两篇文章，这些课文均选自国外知名网站，具有一定的知识性和实用性；New Words and Expressions给出课文中出现的新词，读者由此可以扩充词汇量；Terms对文中出现的专业术语进行解释；Comprehension针对课文进行练习，巩固学习效果；Answers给出参考答案，读者可对照检查学习效果；参考译文帮助读者理解文章大意；常用大数据词汇中英文对照表供读者记忆单词和查询之用。
本书可作为大数据专业相关课程教材，英语专业及计算机专业的选修教材，各类院校大数据和相关专业的参考书，同时也可作为各类计算机从业人员或者有志投身于大数据领域的人士的自学书籍。
本书第1章（Chapter 1）至第8章（Chapter 8）由朱丹编写，第9章（Chapter 9）及常用大数据词汇中英文对照表由王敏编写，第10章（Chapter 10）由张琦编写，第11章（Chapter 11）由陈宏编写。全书由朱丹统稿。
本书文章节选自互联网，在此向文章原作者表示感谢，由于作者水平有限，书中难免出现不足之处，敬请读者不吝指正。
编者
2018年6月
免费在线读
hapter 3
Big Data Analytics
Text A
Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data1, as well as other forms of data that may be untapped by conventional business intelligence(BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things.
Semi-structured and unstructured data may not fit well in traditional data warehouses based on relational databases2. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually – for example, real-time data on the performance of mobile applications or of oil and gas pipelines. As a result, many organizations looking to collect, process and analyze big data have turned to a newer class of technologies that includes Hadoop3 and related tools such as YARN, MapReduce4, Spark5, Hive6 and Pig7 as well as NoSQL database8. Those technologies form the core of an open source software framework that supports the processing of large and diverse data sets across clustered systems.
In some cases, Hadoop clusters and NoSQL systems are being used as landing pads and staging areas for data before it gets loaded into a data warehouse for analysis, often in a summarized form that is more conducive to relational structures. Increasingly though, big data vendors are pushing the concept of a Hadoop data lake that serves as the central repository for an organization’s incoming streams of raw data. In such architectures, subsets of the data can then be filtered for analysis in data warehouses and analytical databases, or it can be analyzed directly in Hadoop using batch query tools, stream processing software and SQL9 on Hadoop technologies that run interactive, ad hoc queries10 written in SQL.
Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process.
Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals. The amount of information that’s typically involved, and its variety, can also cause data management headaches, including data quality and consistency issues. In addition, integrating Hadoop systems and data warehouses can be a challenge, although various vendors now offer software connectors between Hadoop and relational databases, as well as other data integration tools with big data capabilities.
Why is big data analytics important?
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:
(1) Cost reduction. Big data technologies such as Hadoop and cloud-based analytics11 bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
(2) Faster, better decision making. With the speed of Hadoop and in-memory analytics12, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
(3) New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
Big Data Analytics
Text A
Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data1, as well as other forms of data that may be untapped by conventional business intelligence(BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things.
Semi-structured and unstructured data may not fit well in traditional data warehouses based on relational databases2. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually – for example, real-time data on the performance of mobile applications or of oil and gas pipelines. As a result, many organizations looking to collect, process and analyze big data have turned to a newer class of technologies that includes Hadoop3 and related tools such as YARN, MapReduce4, Spark5, Hive6 and Pig7 as well as NoSQL database8. Those technologies form the core of an open source software framework that supports the processing of large and diverse data sets across clustered systems.
In some cases, Hadoop clusters and NoSQL systems are being used as landing pads and staging areas for data before it gets loaded into a data warehouse for analysis, often in a summarized form that is more conducive to relational structures. Increasingly though, big data vendors are pushing the concept of a Hadoop data lake that serves as the central repository for an organization’s incoming streams of raw data. In such architectures, subsets of the data can then be filtered for analysis in data warehouses and analytical databases, or it can be analyzed directly in Hadoop using batch query tools, stream processing software and SQL9 on Hadoop technologies that run interactive, ad hoc queries10 written in SQL.
Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process.
Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals. The amount of information that’s typically involved, and its variety, can also cause data management headaches, including data quality and consistency issues. In addition, integrating Hadoop systems and data warehouses can be a challenge, although various vendors now offer software connectors between Hadoop and relational databases, as well as other data integration tools with big data capabilities.
Why is big data analytics important?
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:
(1) Cost reduction. Big data technologies such as Hadoop and cloud-based analytics11 bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
(2) Faster, better decision making. With the speed of Hadoop and in-memory analytics12, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
(3) New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
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