The Total Data Quality Framework from Coursera

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.

What's inside

Syllabus

Introduction, Different Types of Data and the Total Data Quality Framework

Welcome to the Total Data Quality Framework Course! This is the first course in the Total Data Quality Specialization. This week, you’ll get to know your instructors after reviewing the course syllabus and the learning goals. We will then introduce you to the basic components of the Total Data Quality (TDQ) Framework through a series of video lectures, including Designed Data, Gathered Data, and Hybrid Data. Next, we’ll provide a high-level overview of the TDQ Framework and incorporate the perspectives of global TDQ experts in both a lecture and an interview. We’ll then wrap up the week with a short quiz about measurement and representation concepts.

Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing

In Week 2, we’ll explore the concepts of validity, data origin, and data processing. First, we’ll define validity and discuss threats to validity for designed data and gathered data. We’ll also explore validity through an interview, a real-world application, and a case study. After taking a short quiz to test your knowledge of validity, you’ll then move to the data origin module. We’ll define data processing and explore data origin threats for designed and gathered data through a series of video lectures and case studies. The data processing module will conclude with a short quiz. Week 2 will conclude with an exploration of data processing; data processing threats for designed and gathered data; case studies; and a quiz to check your understanding of data processing.

Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness

This week, we’ll be exploring three representation dimensions of the TDQ framework along with potential threats to data quality. First, we’ll define and discuss data access - as well as data access threats for gathered and designed data - through a series of video lectures, readings, and case studies. After you complete a quiz on data access, we’ll then define data sources and explore data threats for designed and gathered data, along with two case studies. Lastly, we’ll define data missingness along with data missingness threats for designed and gathered data, and then conclude the week with a quiz.

Data Analysis as an Important Aspect of TDQ

We’ll be wrapping up the Total Data Quality Framework course this week. We’ll be discussing why data analysis is a critical dimension of the TDQ framework and threats to data analysis quality for designed and gathered data. You’ll also be reviewing several case studies and will be able to complete an optional tutorial using free R software. After a short quiz on data analysis threats, we’ll conclude the course with a list of references from across Course 1 and we’ll ask you to complete a course survey.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops an understanding of methods for maintaining data quality for both designed and gathered data

Taught by respected instructors from the University of Michigan, who are recognized for their work in total data quality

Covers key dimensions of the Total Data Quality (TDQ) Framework

Provides learners with a strong foundation in data quality principles that can be applied in various fields and industries

Teaches learners how to evaluate data quality threats and develop strategies to mitigate them

May require some background knowledge in data science or statistics, as it assumes familiarity with certain concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Total Data Quality Framework with these activities:

Revisit Previous Coursework on Data Collection Techniques

Show steps

Review previous coursework on data collection techniques to enhance your understanding of the concepts discussed in this course.

Browse courses on Data Collection

Show steps

Review notes or textbooks from previous courses on data collection.
Reflect on the strengths and weaknesses of different data collection methods.
Consider how data collection techniques impact data quality.

Review Basic Concepts of Validity

Show steps

Refresh your understanding of validity to strengthen your foundation for the course.

Browse courses on Validity

Show steps

Review the definition and types of validity.
Consider examples of valid and invalid measures.
Discuss the importance of validity in research.

Organize Course Notes and Materials

Show steps

Organize your course notes, assignments, and other materials to aid in your understanding and retention of the course content.

Show steps

Create a system for organizing your materials.
Review your notes regularly and highlight key concepts.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Complete Practice Quizzes on Data Quality Dimensions

Show steps

Complete practice quizzes on different dimensions of data quality to solidify your understanding and identify areas for improvement.

Browse courses on Validity

Show steps

Review the course materials on data quality dimensions.
Take the practice quizzes on validity, data origin, data processing, data access, data source, and data missingness.
Review your answers and identify any areas where you need further clarification.

Participate in a Study Group

Show steps

Engage with your peers through a study group to enhance your understanding and retention of the course material.

Show steps

Join or form a study group with classmates.
Schedule regular meetings to discuss the course material.
Share notes, insights, and perspectives with each other.

Review Case Studies on Data Quality Issues

Show steps

Review case studies on real-world data quality issues to gain practical insights into the challenges and best practices of data quality management.

Browse courses on Validity

Show steps

Identify a case study that resonates with your interests or industry.
Read through the case study and identify the data quality issues encountered.
Analyze the steps taken to address the data quality issues.
Reflect on the lessons learned and how they can be applied to your own work.

Develop a Data Quality Assessment Plan

Show steps

Develop a data quality assessment plan to apply the concepts learned in the course to a real-world dataset.

Browse courses on Validity

Show steps

Select a dataset that you are familiar with or that interests you.
Identify the specific data quality dimensions that are relevant to your dataset.
Develop a plan for assessing the quality of the data along each dimension.
Implement your plan and document your findings.

Contribute to Open-Source Data Quality Projects

Show steps

Apply your knowledge of data quality by contributing to open-source projects focused on data quality improvement.

Browse courses on Data Quality

Show steps

Identify open-source data quality projects that align with your interests.
Review the project documentation and identify areas where you can contribute.
Contribute to the project by submitting code, documentation, or other resources.

Career center

Learners who complete The Total Data Quality Framework will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use their skills in mathematics, statistics, and computer science to examine, clean, and interpret data. This course helps Data Analysts build a foundation in understanding data and how to measure its quality through various methods. With strong data quality skills, Data Analysts can make better-informed insights and recommendations.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists apply scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. This course provides Data Scientists with a framework for evaluating the quality of data, ensuring they are using credible data in their models and analyses.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers design, build, and maintain the infrastructure and systems that store and process data. This course provides Data Engineers with the knowledge to assess the quality of data before it is stored in data warehouses and data lakes, ensuring the data is reliable and accurate.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts use data to identify business needs and opportunities. This course provides Business Analysts with the skills to evaluate the quality of data, ensuring they are making recommendations based on accurate and reliable information.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers collect, analyze, and interpret data to understand market trends and customer behavior. This course provides Market Researchers with the framework to assess the quality of data, ensuring they are making informed decisions based on reliable information.

See salaries and explore the career path for Market Researcher

Statistician

Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. This course provides Statisticians with an in-depth understanding of data quality, enabling them to ensure the accuracy and reliability of their statistical analyses.

See salaries and explore the career path for Statistician

Data Governance Analyst

Data Governance Analysts ensure that an organization's data is used effectively and ethically. This course may be useful for Data Governance Analysts, as it provides a framework for evaluating data quality and ensuring compliance with data governance policies.

See salaries and explore the career path for Data Governance Analyst

Project Manager

Project Managers oversee the planning, execution, and completion of projects. This course may be useful for Project Managers, as it provides a framework for assessing the quality of project data, ensuring accurate project planning and decision-making.

See salaries and explore the career path for Project Manager

Chief Data Officer

Chief Data Officers are responsible for overseeing an organization's data strategy and data management practices. This course may be helpful for Chief Data Officers, as it provides a comprehensive understanding of data quality and how to implement effective data quality initiatives.

See salaries and explore the career path for Chief Data Officer

Data Architect

Data Architects design and implement data management solutions. This course may be useful for Data Architects, as it provides a framework for assessing the quality of data and designing data architectures that ensure data quality.

See salaries and explore the career path for Data Architect

Data Quality Analyst

Data Quality Analysts assess the quality of data and identify opportunities for improvement. This course provides a comprehensive understanding of data quality and how to implement effective data quality practices.

See salaries and explore the career path for Data Quality Analyst

Database Administrator

Database Administrators maintain and optimize databases. This course may be useful for Database Administrators, as it provides a framework for assessing the quality of data in databases and implementing data quality best practices.

See salaries and explore the career path for Database Administrator

Information Security Analyst

Information Security Analysts protect an organization's information systems and data from unauthorized access. This course may be useful for Information Security Analysts, as it provides a framework for assessing the quality of data and ensuring the confidentiality, integrity, and availability of information systems.

See salaries and explore the career path for Information Security Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers, as it provides a framework for assessing the quality of data used in software applications and implementing data quality best practices in software development.

See salaries and explore the career path for Software Engineer

IT Manager

IT Managers plan and direct the activities of an organization's IT department. This course may be useful for IT Managers, as it provides a framework for assessing the quality of data used in IT systems and implementing data quality best practices throughout the organization.

See salaries and explore the career path for IT Manager