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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/design-strategies-for-maximizing-total-data-quality
课程评论：没有评论
课程名称：最大化整体数据质量的设计策略 课程概述：在整体数据质量专项的第三门课程结束时，学习者将能够： 1. 了解在数据收集或数据收集过程中，最大化整体数据质量（TDQ）的设计工具和技术。 2. 识别影响TDQ的数据生成或收集过程的方面，并评估这些方面是否及如何能够被测量。 3. 理解在收集设计数据和发现/有机数据时可以应用的TDQ最大化策略。 4. 针对数据收集或处理过程中出现的假设设计问题，制定解决方案。 整体专项旨在深入探讨整体数据质量框架，为学习者提供有关在进行数据分析之前需要对整体数据质量进行详细评估的更多信息。目标是让学习者将数据质量评估纳入他们的流程，作为所有项目的关键组成部分。我们希望将整体数据质量的知识传授给所有学习者，尤其是数据科学家和定量分析师，以便他们能够在数据科学流程的初始步骤中充分理解数据收集与数据质量评估的重要性。我们认为，即使拥有丰富的数据科学技术和统计分析程序的知识，如果收集的数据质量不高，这些知识也无法帮助定量研究。 此专项将集中于任何类型的科学研究中的重要第一步：无论是生成还是收集数据，理解数据的来源，评估数据质量，并在进行任何统计分析或应用数据科学技术以回答研究问题之前采取措施最大化数据质量。鉴于此重点，课程内容将较少涉及数据分析的内容，因为这在许多现有的Coursera专项中已有所覆盖。该专项的主要关注点将集中在理解和最大化数据质量上，以为分析做好准备。 课程大纲： 1. 第一部分：引言及最大化有效性与数据来源质量 - 介绍课程内容，学习如何最大化设计和收集数据的有效性与数据来源质量。 2. 第二部分：最大化处理质量与数据访问质量 - 学习如何优化数据处理质量和数据访问质量，通过视频讲座和实际案例深入理解。 3. 第三部分：最大化数据源质量与最小化数据缺失 - 探索如何最大化数据源质量，同时学习减少数据缺失率。 4. 第四部分：最大化数据分析质量 - 最后学习如何优化数据分析质量，并进行同伴评审作业，巩固学习成果。 课程最终将以总结视频和课程反馈问卷结束。
Part: 1
Title:Introduction and Maximizing Validity and Data Origin Quality
Description:Welcome to Design Strategies for Maximizing Total Data Quality! This is the third and final course in the Total Data Quality Specialization. After viewing a short welcome video, reviewing the course syllabus, and taking a course pre-survey, we’ll begin the course by exploring the topic of validity. You’ll learn how to maximize validity for both designed and gathered data through a series of video lectures, readings, and case studies. We’ll conclude our exploration of validity with a quiz on design strategies for maximizing validity. The second half of Week 1 will focus on data origin. You’ll learn how to maximize data origin quality for designed and gathered data through a series of lectures, examples, and case studies. Week 1 will conclude with a quiz on design strategies for maximizing data origin quality.
Part: 2
Title:Maximizing Processing and Data Access Quality
Description:In Week 2, we’ll learn how to optimize data processing quality. We’ll begin the week with video lectures on how to maximize processing quality for designed and gathered data, along with an example for each type of data. We’ll conclude our discussion of processing with a quiz on design strategies for maximizing processing quality. Then, we’ll learn how to maximize data access quality for designed and gathered data while exploring each type of data through video examples and readings. Week 2 will conclude with a short quiz on strategies for maximizing access quality.
Part: 3
Title:Maximizing Data Source Quality and Minimizing Data Missingness
Description:This week, we’ll learn how to optimize the quality of a data source and minimize missing data rates. First, we’ll explore how to maximize data source quality for designed and gathered data. We’ll mix in a series of examples, readings, and case studies throughout our data source unit and conclude this unit with a quiz on strategies for maximizing source quality. Then, we’ll move on to a discussion of data missingness. We’ll learn how to minimize data missingness for designed and gathered data through a series of video lectures and examples. Week 3 will conclude with a short quiz on strategies for minimizing data missingness.
Part: 4
Title:Maximizing the Quality of Data Analysis
Description:Welcome to the final week of Design Strategies for Maximizing Total Data Quality and the Total Data Quality specialization! We’ll wrap up the series by learning how to optimize data analysis quality for both designed and gathered data. This exploration will include a series of video lectures and case studies. After you take a quiz on how to maximize data analysis quality, you’ll work on a peer review assignment that asks you to review a study of Wordle performance. The week will conclude with a specialization recap video and a course and specialization post-survey.
By the end of this third course in the Total Data Quality Specialization, learners will be able to: 1. Learn about design tools and techniques for maximizing TDQ across all stages of the TDQ framework during a data collection or a data gathering process. 2. Identify aspects of the data generating or data gathering process that impact TDQ and be able to assess whether and how such aspects can be measured. 3. Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data. 4. Develop solutions to hypothetical design problems arising during the process of data collection or data gathering and processing. 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.
