Juniata Classifieds>Juniata Online Courses>The Total Data Quality Framework

The Total Data Quality Framework

About this Course

By the end of this first course in the Total Data Quality specialization, learners will be able to: 1. Identify the essential differences between designed and gathered data and summarize the key dimensions of the Total Data Quality (TDQ) Framework; 2. Define the three measurement dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; 3. Define the three representation dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; and 4. Describe why data analysis defines an important dimension of the Total Data Quality framework, and summarize potential threats to the overall quality of an analysis plan for designed and/or gathered data. 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.

Created by: University of Michigan


Related Online Courses

This course provides the foundation for understanding the frameworks used to develop market risk management strategies. You will identify the market risks associated with each type of financial... more
\"This course delves into Sustainable Practices in Building Design, focusing on the principles of sustainable development and green construction methods. It emphasizes the importance of conserving... more
This Specialization is for learners who want to learn how to identify, analyze, and solve business problems using spreadsheet models. In addition, learners will use mathematical tools to discover... more
This specialization is primarily aimed at first- and second-year undergraduates interested in psychology, data analysis, ethics in research, and quantitative research methods along with high school... more
Code and run your first Java program in minutes without installing anything! This course is designed for learners with limited coding experience, providing a solid foundation of not just Java, but... more

CONTINUE SEARCH

FOLLOW COLLEGE PARENT CENTRAL