Module 1 Summary模块 1 总结

Congratulations! You have completed this module. At this point in the course, you know:
恭喜！您已完成本模块。此时，您在课程中已经知道：

• The Data Science Task Categories include:
  数据科学任务类别包括：
  • Data Management - storage, management and retrieval of data
    数据管理 - 数据的存储、管理和检索
  • Data Integration and Transformation - streamline data pipelines and automate data processing tasks
    数据集成和转换 - 简化数据管道并自动化数据处理任务
  • Data Visualization - provide graphical representation of data and assist with communicating insights
    数据可视化 - 提供数据的图形化表示，并协助传达洞察力
  • Modelling - enable Building, Deployment, Monitoring and Assessment of Data and Machine Learning models
    建模 - 支持数据与机器学习模型的构建、部署、监控和评估
• Data Science Tasks support the following:
  数据科学任务支持以下功能：
  • Code Asset Management - store & manage code, track changes and allow collaborative development
    代码资产管理 - 存储和管理代码，跟踪变更，并支持协作开发
  • Data Asset Management - organize and manage data, provide access control, and backup assets
    数据资产管理 - 组织和管理数据，提供访问控制，并备份资产
  • Development Environments - develop, test and deploy code
    开发环境 - 开发、测试和部署代码
  • Execution Environments - provide computational resources and run the code
    执行环境 - 提供计算资源并运行代码

The data science ecosystem consists of many open source and commercial options, and include both traditional desktop applications and server-based tools, as well as cloud-based services that can be accessed using web-browsers and mobile interfaces.
数据科学生态系统包括许多开源和商业选项，包括传统的桌面应用程序和基于服务器的工具，以及可以通过网络浏览器和移动界面访问的云服务。

Data Management Tools: include Relational Databases, NoSQL Databases, and Big Data platforms:
数据管理工具：包括关系型数据库、NoSQL 数据库和大数据平台：

• MySQL, and PostgreSQL are examples of Open Source Relational Database Management Systems (RDBMS), and IBM Db2 and SQL Server are examples of commercial RDBMSes and are also available as Cloud services.
  MySQL 和 PostgreSQL 是开源关系型数据库管理系统（RDBMS）的例子，IBM Db2 和 SQL Server 是商业 RDBMS 的例子，并且也提供云服务。
• MongoDB and Apache Cassandra are examples of NoSQL databases.
  MongoDB 和 Apache Cassandra 是 NoSQL 数据库的例子。
• Apache Hadoop and Apache Spark are used for Big Data analytics.
  Apache Hadoop 和 Apache Spark 用于大数据分析。

Data Integration and Transformation Tools: include Apache Airflow and Apache Kafka.
数据集成与转换工具：包括 Apache Airflow 和 Apache Kafka。

Data Visualization Tools: include commercial offerings such as Cognos Analytics, Tableau and PowerBI and can be used for building dynamic and interactive dashboards.
数据可视化工具：包括商业产品，如 Cognos Analytics、Tableau 和 PowerBI，可用于构建动态和交互式仪表板。

Code Asset Management Tools: Git is an essential code asset management tool. GitHub is a popular web-based platform for storing and managing source code. Its features make it an ideal tool for collaborative software development, including version control, issue tracking, and project management.
代码资产管理工具：Git 是一种重要的代码资产管理工具。GitHub 是一个流行的基于网络的平台，用于存储和管理源代码。其功能使其成为协作软件开发理想的工具，包括版本控制、问题跟踪和项目管理。

Development Environments: Popular development environments for Data Science include Jupyter Notebooks and RStudio.
开发环境：数据科学领域流行的开发环境包括 Jupyter Notebooks 和 RStudio。

• Jupyter Notebooks provides an interactive environment for creating and sharing code, descriptive text, data visualizations, and other computational artifacts in a web-browser based interface.
  Jupyter Notebooks 提供了一个交互式环境，用于在基于浏览器的界面中创建和共享代码、描述性文本、数据可视化和其他计算工件。
• RStudio is an integrated development environment (IDE) designed specifically for working with the R programming language, which is a popular tool for statistical computing and data analysis.
  RStudio 是一个专为使用 R 编程语言而设计的集成开发环境（IDE），它是统计计算和数据分析的流行工具。