Review what you learned复习所学内容

After completing this course, you learned many facts about data science methodology. Here are 14 key, high-level takeaway facts you’ll want to remember from this course.
完成这门课程后，你学习了关于数据科学方法论的大量事实。以下是这门课程中你需要记住的 14 个关键、高级要点。

• Foundational methodology, a cyclical, iterative data science methodology developed by John Rollins, consists of 10 stages, starting with Business Understanding and ending with Feedback.
  基础方法论，由 John Rollins 开发的一个循环迭代的数据科学方法论，包括 10 个阶段，从业务理解开始，以反馈结束。
• CRISP-DM, an open source data methodology, combines several data-related methodology stages into one stage and omits the Feedback stage resulting in a six-stage data methodology.
  CRISP-DM 是一个开源数据方法论，将多个与数据相关的阶段合并为一个阶段，并省略了反馈阶段，从而形成了一个六阶段的数据方法论。
• The primary goal of the Business Understanding stage is to understand the business problem and determine the data needed to answer the core business question. 
  业务理解阶段的主要目标是理解业务问题，并确定回答核心业务问题所需的数据。
• During the Analytic Approach stage, you can choose from descriptive diagnostic, predictive, and prescriptive analytic approaches and whether to use machine learning techniques.
  在分析方法阶段，您可以选择描述性诊断、预测性和规范性分析方法，并决定是否使用机器学习技术。
• During the Data Requirements stage, scientists identify the correct and necessary data content, formats, and sources needed for the specific analytical approach.
  在数据需求阶段，科学家们识别出特定分析方法所需的正确和必要的数据内容、格式和来源。
• During the Data Collection stage, expert data scientists revise data requirements and make critical decisions regarding the quantity and quality of data. Data scientists apply descriptive statistics and visualization techniques to thoroughly assess the content, quality, and initial insights gained from the collected data, identify gaps, and determine if new data is needed, or if they should substitute existing data.
  在数据收集阶段，专家数据科学家们修订数据需求，并就数据的数量和质量做出关键决策。数据科学家应用描述性统计和可视化技术，全面评估收集到的数据的内容、质量以及初步获得的认识，识别差距，并确定是否需要新的数据，或者是否应该替换现有数据。
• The Data Understanding stage encompasses all activities related to constructing the data set. This stage answers the question of whether the collected data represents the data needed to solve the business problem. Data scientists might use descriptive statistics, predictive statistics, or both.
  数据理解阶段涵盖了与构建数据集相关的所有活动。此阶段回答了收集到的数据是否代表了解决业务问题所需的数据的问题。数据科学家可能会使用描述性统计、预测性统计或两者兼用。
• Data scientists commonly apply Hurst, univariates, and statistics such as mean, median, minimum, maximum, standard deviation, pairwise correlation, and histograms. 
  数据科学家通常应用 Hurst、单变量以及均值、中位数、最小值、最大值、标准差、成对相关性和直方图等统计方法。
• During the Data Preparation stage, data scientists must address missing or invalid values, remove duplicates, and validate that the data is properly formatted. Feature engineering and text analysis are key techniques data scientists apply to validate and analyze data during the Data Preparation stage.
  在数据准备阶段，数据科学家必须处理缺失或无效值，删除重复数据，并验证数据是否正确格式化。特征工程和文本分析是数据科学家在数据准备阶段用于验证和分析数据的关键技术。
• The end goal of the Modeling stage is that the data model answers the business question. During the Modeling stage, data scientists use a training data set. Data scientists test multiple algorithms on the training set data to determine whether the variables are required and whether the data supports answering the business question. The outcome of those models is either descriptive or predictive. 
  建模阶段的目标是数据模型能够回答业务问题。在建模阶段，数据科学家使用训练数据集。数据科学家在训练集数据上测试多种算法，以确定变量是否必要以及数据是否支持回答业务问题。这些模型的输出结果要么是描述性的，要么是预测性的。
• The Evaluation stage consists of two phases, the diagnostic measures phase, and the statistical significance phase. Data scientists and others assess the quality of the model and determine if the model answers the initial Business Understanding question or if the data model needs adjustment. 
  评估阶段包括两个阶段：诊断度量阶段和统计显著性阶段。数据科学家和其他人员评估模型的质量，并确定模型是否回答了初始的业务理解问题，或者数据模型是否需要调整。
• During the Deployment stage, data scientists release the data model to a targeted group of stakeholders, including solution owners, marketing staff, application developers, and IT administration., 
  在部署阶段，数据科学家将数据模型发布给目标利益相关者群体，包括解决方案所有者、市场营销人员、应用程序开发人员和 IT 管理员。
• During the Feedback stage, stakeholders and users evaluate the model and contribute feedback to assess the model’s performance. 
  在反馈阶段，利益相关者和用户评估模型，并向模型性能提供反馈。
• The data model’s value depends on its ability to iterate; that is, how successfully the data model incorporates user feedback.
  数据模型的价值取决于其迭代能力；也就是说，数据模型成功整合用户反馈的程度。