Module 02 · Data Science Topics
Glossary: Big Data and Data Mining术语表：大数据与数据挖掘
Welcome! This glossary contains many of the terms in this lesson. These terms are important for you to recognize when working in the industry, participating in user groups, and participating in other certificate programs.
欢迎！本术语表包含本课程中的许多术语。这些术语对于您在行业内工作、参与用户组以及参与其他证书项目时识别非常重要。
| Term 术语 | Definition 定义 | Video where the term is introduced 介绍该术语的视频 |
|---|---|---|
| Analytics 分析 | The process of examining data to draw conclusions and make informed decisions is a fundamental aspect of data science, involving statistical analysis and data-driven insights. 分析数据以得出结论并做出明智决策是数据科学的一个基本方面，涉及统计分析和数据驱动洞察。 |
Data Scientists at New York University 纽约大学的数据科学家 |
| Big Data 大数据 | Vast amounts of structured, semi-structured, and unstructured data are characterized by its volume, velocity, variety, veracity and value, which, when analyzed, can provide competitive advantages and drive digital transformations. 大量的结构化、半结构化和非结构化数据具有其规模、速度、种类、真实性和价值等特点，当进行分析时，可以提供竞争优势并推动数字化转型。 |
How Big Data is Driving Digital Transformation 大数据如何推动数字化转型 |
| Big Data Cluster 大数据集群 | A distributed computing environment comprising thousands or tens of thousands of interconnected computers that collectively store and process large datasets. 一个由成千上万台互联计算机组成的分布式计算环境，这些计算机共同存储和处理大型数据集。 |
What is Hadoop? 什么是 Hadoop？ |
| Broad Network Access 广泛的网络访问 | The ability to access cloud resources via standard mechanisms and platforms such as mobile devices, laptops, and workstations over networks. 通过标准机制和平台（如移动设备、笔记本电脑和工作站）在网络访问云资源的能力。 |
Introduction to Cloud 云计算简介 |
| Chief Data Officer (CDO) 首席数据官（CDO） |
An emerging role responsible for overseeing data-related initiatives, governance, and strategies, ensuring that data plays a central role in digital transformation efforts. 一个新兴的角色，负责监督数据相关计划、治理和战略，确保数据在数字化转型中发挥核心作用。 |
How Big Data is Driving Digital Transformation 大数据如何推动数字化转型 |
| Chief Information Officer (CIO) 首席信息官（CIO） |
An executive is responsible for managing an organization's information technology and computer systems, contributing to technology-related aspects of digital transformation. 高管负责管理组织的信息技术和计算机系统，为数字化转型中的技术相关方面做出贡献。 |
How Big Data is Driving Digital Transformation 大数据如何推动数字化转型 |
| Cloud Computing 云计算 | The delivery of on-demand computing resources, including networks, servers, storage, applications, services, and data centers, over the Internet on a pay-for-use basis. 在互联网上按需付费使用计算资源（包括网络、服务器、存储、应用程序、服务和数据中心）的交付方式。 |
Introduction to Cloud 云计算简介 |
| Cloud Deployment Models 云部署模型 | Categories that indicate where cloud infrastructure resides, who manages it, and how cloud resources and services are made available to users, including public, private, and hybrid models. 表明云基础设施的存放位置、管理方以及云资源和服务如何提供给用户的类别，包括公有云、私有云和混合云模型。 |
Introduction to Cloud 云计算简介 |
| Cloud Service Models 云服务模型 | Models based on the layers of a computing stack, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), represent different cloud computing offerings. 基于计算堆栈各层的模型，包括基础设施即服务（IaaS）、平台即服务（PaaS）和软件即服务（SaaS），代表了不同的云计算服务。 |
Introduction to Cloud 云计算简介 |
| Commodity Hardware 商品硬件 | Standard, off-the-shelf hardware components are used in a big data cluster, offering cost-effective solutions for storage and processing without relying on specialized hardware. 大数据集群中使用标准、现成的硬件组件，为存储和处理提供经济高效的解决方案，无需依赖专用硬件。 |
What is Hadoop? 什么是 Hadoop？ |
| Data Algorithms 数据算法 | Computational procedures and mathematical models used to process and analyze data made accessible in the cloud for data scientists to deploy on large datasets efficiently. 用于处理和分析云中可访问数据、使数据科学家能够高效在大数据集上部署的计算流程和数学模型。 |
Cloud for Data Science 数据科学云 |
| Data Replication 数据复制 | A strategy in which data is duplicated across multiple nodes in a cluster to ensure data durability and availability, reducing the risk of data loss due to hardware failures. 一种在集群中的多个节点上复制数据以确保数据持久性和可用性的策略，减少因硬件故障导致数据丢失的风险。 |
What is Hadoop? 什么是 Hadoop？ |
| Data Science 数据科学 | An interdisciplinary field that involves extracting insights and knowledge from data using various techniques, including programming, statistics, and analytical tools. 一个涉及使用各种技术（包括编程、统计学和分析工具）从数据中提取见解和知识的跨学科领域。 |
Data Scientists at New York University 纽约大学的数据科学家 |
| Deep Learning 深度学习 | A subset of machine learning that involves artificial neural networks inspired by the human brain, capable of learning and making complex decisions from data on their own. 机器学习的一个子集，涉及受人类大脑启发的人工神经网络，能够从数据中自行学习和做出复杂决策。 |
Data Scientists at New York University 纽约大学的数据科学家 |
| Digital Change 数字变革 | The integration of digital technology into business processes and operations leads to improvements and innovations in how organizations operate and deliver value to customers. 将数字技术整合到业务流程和运营中，推动了组织运营方式和为客户创造价值方法的改进和创新。 |
How Big Data is Driving Digital Transformation 大数据如何驱动数字化转型 |
| Digital Transformation 数字化转型 | A strategic and cultural organizational change driven by data science, especially Big Data, to integrate digital technology across all areas of the organization, resulting in fundamental operational and value delivery changes. 由数据科学，尤其是大数据驱动的一种战略和文化组织变革，旨在将数字技术整合到组织的所有领域，从而实现根本性的运营和价值创造变革。 |
How Big Data is Driving Digital Transformation 大数据如何驱动数字化转型 |
| Distributed Data 分布式数据 | The practice of dividing data into smaller chunks and distributing them across multiple computers within a cluster enables parallel processing for data analysis. 将数据分割成更小的块并在集群内的多台计算机上分布它们，可以实现数据分析的并行处理。 |
What is Hadoop? 什么是 Hadoop？ |
| Hadoop | A distributed storage and processing framework used for handling and analyzing large datasets, particularly well-suited for big data analytics and data science applications. 一个分布式存储和处理框架，用于处理和分析大型数据集，特别适用于大数据分析和数据科学应用。 |
Data Scientists at New York University 纽约大学的数据科学家 |
| Hadoop Distributed File System (HDFS) Hadoop 分布式文件系统（HDFS） |
A storage system within the Hadoop framework that partitions and distributes files across multiple nodes, facilitating parallel data access and fault tolerance. Hadoop 框架中的一个存储系统，将文件分区并分布到多个节点上，促进并行数据访问和容错。 |
What is Hadoop? 什么是 Hadoop？ |
| Infrastructure as a Service (IaaS) 基础设施即服务（IaaS） |
A cloud service model that provides access to computing infrastructure, including servers, storage, and networking, without the need for users to manage or operate them. 一种云服务模型，提供对计算基础设施的访问，包括服务器、存储和网络，而无需用户管理或操作它们。 |
Introduction to Cloud 云计算简介 |
| Java-Based Framework 基于 Java 的框架 | Hadoop is implemented in Java, an open-source, high-level programming language, providing the foundation for building distributed storage and processing solutions. Hadoop 是用 Java 这种开源的高级编程语言实现的，为构建分布式存储和处理解决方案提供了基础。 |
Big Data Processing Tools: Hadoop, HDFS, Hive, and Spark 大数据处理工具：Hadoop、HDFS、Hive 和 Spark |
| Map Process Map 过程 | The initial step in Hadoop's MapReduce programming model, where data is processed in parallel on individual cluster nodes, often used for data transformation tasks. Hadoop 的 MapReduce 编程模型中的初始步骤，在单个集群节点上并行处理数据，通常用于数据转换任务。 |
What is Hadoop? 什么是 Hadoop？ |
| Measured Service 测量服务 | A characteristic where users are billed for cloud resources based on their actual usage, with resource utilization transparently monitored, measured, and reported. 一种特征，即用户根据实际使用情况支付云资源费用，同时资源利用率被透明地监控、测量和报告。 |
Introduction to Cloud 云服务简介 |
| On-Demand Self-Service 按需自助服务 | The capability for users to access and provision cloud resources such as processing power, storage, and networking using simple interfaces without human interaction with service providers. 用户能够通过简单界面访问和配置云资源（如处理能力、存储和网络），而无需与服务提供者进行人工交互的能力。 |
Introduction to Cloud 云计算简介 |
| Rapid Elasticity 快速弹性 | The ability to quickly scale cloud resources up or down based on demand, allowing users to access more resources when needed and release them when not in use. 根据需求快速扩展或缩减云资源的能力，使用户能够在需要时获取更多资源，在不需要时释放资源。 |
Introduction to Cloud 云计算简介 |
| Reduce Process Reduce 过程 | The second step in Hadoop's MapReduce model is where results from the mapping process are aggregated and processed further to produce the final output, typically used for analysis. Hadoop 的 MapReduce 模型中的第二步是将映射过程中的结果进行聚合和进一步处理，以生成最终输出，通常用于分析。 |
What is Hadoop? 什么是 Hadoop？ |
| Replication 复制 | The act of creating copies of data pieces within a big data cluster enhances fault tolerance and ensures data availability in case of hardware or node failures. 在大型数据集群中创建数据副本的行为增强了容错能力，并确保在硬件或节点故障时数据仍然可用。 |
What is Hadoop? 什么是 Hadoop？ |
| Resource Pooling 资源池化 | A cloud characteristic where computing resources are shared and dynamically assigned to multiple consumers, promoting economies of scale and cost-efficiency. 云计算的一个特征，其中计算资源被共享并动态分配给多个消费者，促进规模经济和成本效益。 |
Introduction to Cloud 云计算简介 |
| Skills Network Labs (SN Labs) 技能网络实验室（SN Labs） |
Learning resources provided by IBM, including tools like Jupyter Notebooks and Spark clusters, are available to learners for cloud data science projects and skill development. IBM 提供的包括 Jupyter Notebooks 和 Spark 集群在内的学习资源，可供学习者用于云数据科学项目和技能发展。 |
Cloud for Data Science 数据科学云 |
| Spilling to Disk 溢写到磁盘 | A technique used in memory-constrained situations where data is temporarily written to disk storage when memory resources are exhausted, ensuring uninterrupted processing. 一种在内存受限情况下使用的技巧，当内存资源耗尽时，将数据临时写入磁盘存储，以确保处理不中断。 |
Big Data Processing Tools: Hadoop, HDFS, Hive, and Spark 大数据处理工具：Hadoop、HDFS、Hive 和 Spark |
| STEM Classes STEM 课程 | Science, Technology, Engineering, and Mathematics (STEM) courses typically taught in high schools prepare students for technical careers, including data science. 科学、技术、工程和数学（STEM）课程通常在高中教授，旨在为学生提供技术职业的培训，包括数据科学。 |
Data Scientists at New York University 纽约大学的数据科学家 |
| Variety 多样性 | The diversity of data types, including structured and unstructured data from various sources such as text, images, video, and more, posing data management challenges. 数据类型的多样性，包括来自文本、图像、视频等各种来源的结构化和非结构化数据，给数据管理带来了挑战。 |
Foundations of Big Data 大数据基础 |
| Velocity 速度 | The speed at which data accumulates and is generated, often in real-time or near-real-time, drives the need for rapid data processing and analytics. 数据积累和生成的速度，通常为实时或近实时，推动了快速数据处理和分析的需求。 |
Foundations of Big Data 大数据基础 |
| Veracity 真实性 | The quality and accuracy of data, ensuring that it conforms to facts and is consistent, complete, and free from ambiguity, impacts data reliability and trustworthiness. 数据的品质和准确性，确保其符合事实，并且一致、完整、无歧义，影响数据的可靠性和可信度。 |
Foundations of Big Data 大数据基础 |
| Video Tracking System 视频跟踪系统 | A system used to capture and analyze video data from games, enabling in-depth analysis of player movements and game dynamics, contributing to data-driven decision-making in sports. 一个用于捕获和分析游戏视频数据的系统，能够深入分析玩家动作和游戏动态，为体育领域的数据驱动决策提供支持。 |
How Big Data is Driving Digital Transformation 大数据如何驱动数字化转型 |
| Volume 规模 | The scale of data generated and stored is driven by increased data sources, higher-resolution sensors, and scalable infrastructure. 数据生成和存储的规模是由增加的数据源、更高分辨率的传感器和可扩展的基础设施驱动的。 |
Foundations of Big Data 大数据基础 |
| V's of Big Data 大数据的 V |
A set of characteristics common across Big Data definitions, including Velocity, Volume, Variety, Veracity, and Value, highlighting the rapid generation, scale, diversity, quality, and value of data. 一组在 Big Data 定义中普遍存在的特征，包括速度（Velocity）、规模（Volume）、多样性（Variety）、真实性（Veracity）和价值（Value），突出了数据的快速生成、规模、多样性、质量和价值。 |
Foundations of Big Data 大数据的基础理论 |