Concept of overall data quality

By the end of this first course in the Total Data Quality specialization, students will be able to: 1. Identify significant differences between developed and captured data and briefly describe the key dimensions of the Total Data Quality (TDQ) Framework; 2. Identify the three dimensions of Total Data Quality and describe potential threats to data quality along each of these dimensions for both collected and developed data; 3. Identify the three aspects of data presentation in the Total Data Quality Framework and describe potential threats to data quality along each of these dimensions for both collected and developed data; and 4. Describe why data analysis is an important dimension of the Total Data Quality Framework and briefly describe potential threats to the overall quality of the analysis plan for developed and/or collected data. The overall focus of this specialization is to provide an in-depth examination of the Total Data Quality Framework and to provide students with additional information about the detailed overall data quality assessment that should be conducted prior to beginning data analysis. The goal is for students to incorporate data quality assessment into their process as a critical component of all projects. We sincerely hope to spread knowledge about general data quality to all learners, such as data scientists and quantitative analysts, who have not received sufficient training in the initial stages of the data science process that focus on collecting data and assessing its quality. We believe that extensive knowledge of data science methods and statistical analysis procedures will not help in conducting quantitative research if the data collected/gathered is not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific research using data: generating or collecting data, understanding where the data comes from, assessing the quality of the data, and taking steps to maximize the quality of the data before conducting any kind of statistical analysis or applying data science methods to answer research questions. Given this focus, the course will contain little material on data analysis, which is covered in numerous existing Coursera specializations. The focus of this specialization will be on understanding and maximizing the quality of the data before analyzing it.