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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/measuring-total-data-quality
课程评论：没有评论
课程名称：测量总体数据质量 概述： 在本课程中，学习者将掌握评估总体数据质量（TDQ）框架各阶段的多种指标。完成后，学员们将能： 1. 了解评估TDQ的不同指标。 2. 创建涵盖特定应用或数据源相关方面的质量概念图。 3. 思考质量各方面之间的相对权衡，考虑特定项目或研究的成本与实际限制。 4. 确定计算各种指标的相关软件和工具。 5. 了解可针对设计数据与发现/生成数据计算的指标。 6. 将指标应用于真实数据，并从TDQ的角度解释其结果值。 本专业化课程深入探讨总体数据质量框架，并为学习者提供有关在数据分析前需要进行的详细总体数据质量评估的信息。目标是使学习者将数据质量评估纳入项目过程，作为一个关键组件。本课程特别希望向所有学习者（如数据科学家和定量分析师）传播关于总体数据质量的知识，尤其是那些在数据收集与质量评估初步步骤方面培训不足的人。我们认为，广泛的数据科学技术和统计分析程序知识如果没有高质量的数据支持，将无法推动定量研究。 本专业化课程重点关注任何类型的科学研究中的关键第一步：生成或收集数据、理解数据来源、评估数据质量，以及在进行任何统计分析或应用数据科学技术之前提升数据质量。因此，本课程将较少涉及数据分析的内容，因为这些内容在许多现有的Coursera专业化课程中已有广泛覆盖。主要的重点将放在理解和最大化数据质量，以便为后续分析做好准备。 课程大纲： 第一部分：介绍及测量有效性和数据来源质量 学习测量设计和收集数据的有效性，完成相关的测试和测量数据来源质量的模块。 第二部分：测量处理和数据访问质量 重点讨论设计和收集数据的处理质量与访问质量，包含案例研究和测试。 第三部分：测量数据来源质量和数据缺失性 学习如何测量数据来源质量和数据缺失性，包括实际数据的计算实例和相关测试。 第四部分：测量数据分析质量 最终周学习如何测量设计和收集数据分析的质量，并完成相关的阅读与测试。 此课程为希望深入理解数据质量评估的学习者提供了实用的指导和工具。
Part: 1
Title:Introduction and Measuring Validity and Data Origin Quality
Description:Welcome to Measuring Total Data Quality! This is the second course in the Total Data Quality Specialization. After reviewing the Course 2 syllabus and completing the course pre-survey, you’ll learn how to measure validity for designed and gathered data through a series of video lectures, examples, and readings. You’ll then take a short quiz on interpreting validity metrics. Then, you’ll complete a module on data origin, where you’ll learn about measuring data origin quality for designed and gathered data in a series of video lectures and case studies. Week 1 will conclude with a quiz on interpreting data origin quality metrics.
Part: 2
Title:Measuring Processing and Data Access Quality
Description:Welcome to Week 2 of Measuring Total Data Quality! We’ll begin the week by discussing how to measure processing data quality for designed and gathered data. We’ll include examples of measuring process data quality for each form of data and conclude the module with a quiz on interpreting processing metrics. In the second half of Week 2, we’ll discuss measuring data access quality for designed and gathered data through video lectures, an example, and a case study, and conclude the week with a quiz on interpreting access metrics.
Part: 3
Title:Measuring Data Source Quality and Data Missingness
Description:This week, we’ll learn how to measure data source quality and data missingness. We’ll begin Week 3 with a video lecture on measuring data source quality for designed data. Then, we’ll work through an example of computing data source metrics with real data and code. We’ll then learn how to measure data source quality for gathered data and see an example of computer data source quality metrics with real data and code. You’ll then take a short quiz on interpreting data source quality metrics and move on to the Data Missingness unit. We’ll learn how to measure threats to data source quality for designed and gathered data and work through examples for each form of data. Week 3 will conclude with a quiz on interpreting data missingness metrics.
Part: 4
Title:Measuring the Quality of Data Analysis
Description:We’ll be wrapping up Measuring Total Data Quality this week by learning how to measure the quality of data analysis. We’ll learn how to measure the quality of data analysis for designed and gathered data and work through examples of each type of data. We recommend that you complete two readings before you complete the lecture on measuring the quality of analysis for gathered data. We will conclude the week with a quiz on examining quality metrics and interpreting output, as well as references for the Measuring Total Data Quality course and a course post-survey.
By the end of this second course in the Total Data Quality Specialization, learners will be able to: 1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework. 2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source. 3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study. 4. Identify relevant software and related tools for computing the various metrics. 5. Understand metrics that can be computed for both designed and found/organic data. 6. Apply the metrics to real data and interpret their resulting values from a TDQ perspective. This specialization as a whole aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
