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.
