Summary and Highlights总结与要点

In this lesson, you have learned:
在本课程中，您已学习：

Depending on where the data must be sourced from, there are a number of methods and tools available for gathering data. These include query languages for extracting data from databases, APIs, Web Scraping, Data Streams, RSS Feeds, and Data Exchanges.
根据数据来源的不同，有多种方法和工具可用于数据收集。这些方法和工具包括用于从数据库中提取数据的查询语言、API、网络爬虫、数据流、RSS 订阅源和数据交换。

Once the data you need has been gathered and imported, your next step is to make it analytics-ready. This is where the process of Data Wrangling, or Data Munging, comes in. Data Wrangling involves a whole range of transformations and cleansing activities performed on the data. Transformation of raw data includes the tasks you undertake to:
一旦所需数据被收集和导入，下一步就是使其具备分析就绪性。这就是数据规整（Data Wrangling）或数据清洗（Data Munging）过程的用武之地。 数据规整涉及对数据进行一系列的转换和清洗活动。原始数据的转换包括您为以下目的所执行的任务：

• Structurally manipulate and combine data using Joins and Unions.
  使用连接（Joins）和联合（Unions）对数据进行结构性操作和组合。
• Normalize data, that is, clean the database of unused and redundant data.
  规范化数据，即清理数据库中未使用的冗余数据。
• Denormalize data, that is, combine data from multiple tables into a single table so that it can be queried faster.
  反规范化数据，即将来自多个表的数据合并到一个表中，以便更快地查询。

Cleansing activities include:
清洗活动包括：

• Profiling data to uncover anomalies and quality issues.
  分析数据以发现异常和质量问题。
• Visualizing data using statistical methods in order to spot outliers.
  使用统计方法可视化数据以发现异常值。
• Fixing issues such as missing values, duplicate data, irrelevant data, inconsistent formats, syntax errors, and outliers.
  修复缺失值、重复数据、不相关数据、不一致格式、语法错误和异常值等问题。

A variety of software and tools are available for the data wrangling process. Some of the popularly used ones include Excel Power Query, Spreadsheets, OpenRefine, Google DataPrep, Watson Studio Refinery, Trifacta Wrangler, Python, and R, each with their own set of features, strengths, limitations, and applications.
有各种软件和工具可用于数据整理过程。一些常用的包括 Excel Power Query、电子表格、OpenRefine、Google DataPrep、Watson Studio Refinery、Trifacta Wrangler、Python 和 R，每种都有其自身的功能、优势、局限性和应用。