By the end of this third course in the Total Data Quality Specialization, learners will be able to: 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; 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; understand TDQ maximization strategies that can be applied when gathering designed and found/organic data; develop solutions to hypothetical design problems arising during the process of data collection or data gathering and processing.
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Course 3 of 3 in the Total Data Quality Specialization
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.
Syllabus
WEEK 1
Introduction and Maximizing Validity and Data Origin Quality
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.
WEEK 2
Maximizing Processing and Data Access Quality
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.
WEEK 3
Maximizing Data Source Quality and Minimizing Data Missingness
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.
WEEK 4
Maximizing the Quality of Data Analysis
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.
