We may earn an affiliate commission when you visit our partners.
Brady T. West,
James Wagner,
Jinseok Kim,
and
Trent D Buskirk
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;
Read more
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;
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/y27dvq/the
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.
Read more
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.
Read more
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
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
Save this course
Save The Total Data Quality Framework to your list so you can find it easily later:
Save
Save
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.
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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 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.
Data Scientist
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.
Data Engineer
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.
Business Analyst
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.
Market Researcher
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.
Statistician
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.
Data Governance Analyst
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.
Project Manager
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.
Chief Data Officer
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.
Data Architect
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.
Data Quality Analyst
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.
Database Administrator
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.
Information Security Analyst
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.
Software Engineer
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.
IT Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/y27dvq/the
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
The Total Data Quality Framework.
Provides a comprehensive overview of data quality concepts and techniques, covering various dimensions of data quality, including measurement, representation, and analysis.
This field guide is valuable because it includes templates and useful methods that might be less well-known to learners and researchers in the field.
While this book centers on dimensional modeling, it provides a valuable background on data warehousing, data modeling concepts, and best practices, which are fundamental to understanding data quality within the context of data management and analysis.
Provides a theoretical foundation for data quality assessment and covers various methods and techniques for evaluating data quality. It can serve as a reference for understanding the measurement and representation dimensions of the TDQ Framework.
While Wang's book and the TDQ framework course both concentrate on data quality, this book is more specifically targeted at data management and how to improve it. It would thus serve as a useful adjunct text for the course.
While this book is primarily focused on statistical learning methods, it also covers topics related to data quality, such as data preprocessing and feature selection. It can provide additional context for the discussion on data analysis as an important aspect of TDQ.
Save
Introduces machine learning concepts and techniques using the R programming language. It covers data cleaning, data exploration, and model building, which are all important aspects of data quality management.
Similar to the previous book, this one provides an introduction to data science using the Python programming language. It covers data cleaning, data visualization, and machine learning, which are relevant to the practical aspects of data quality management.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y27dvq/the
Share
Similar courses
Here are nine courses similar to
The Total Data Quality Framework.
Design Strategies for Maximizing Total Data Quality
OpenCourser.com/course/hgya76/design
Design Strategies for Maximizing Total Data Quality
Most relevant
Measuring Total Data Quality
OpenCourser.com/course/8r5za3/measuring
Measuring Total Data Quality
Most relevant
Capstone - Managing Board Change for Higher Levels of Leadership and Governance Effectiveness
OpenCourser.com/course/dj3gp3/capstone
Capstone - Managing Board Change for Higher Levels of...
The DMAIC Framework - Define and Measure Phase
OpenCourser.com/course/jayd0e/the
The DMAIC Framework - Define and Measure Phase
Survey Data Collection and Analytics Project (Capstone)
OpenCourser.com/course/35a5gx/survey
Survey Data Collection and Analytics Project (Capstone)
DataOps Methodology
OpenCourser.com/course/b21bpl/dataops
DataOps Methodology
Healthcare Data Quality and Governance
OpenCourser.com/course/lclyup/healthcare
Healthcare Data Quality and Governance
Advanced Quantitative Statistics with Excel
OpenCourser.com/course/jrcqki/advanced
Advanced Quantitative Statistics with Excel
Exploratory Data Analysis in AWS
OpenCourser.com/course/fwj1fy/exploratory
Exploratory Data Analysis in AWS
Effort
4 weeks of study, 1.5-4 hours per week
Level
Introductory
Via
Coursera
Institution
University of Michigan
Instructors
Brady T. West
James Wagner
Jinseok Kim
Trent D Buskirk
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
Total Data Quality. It's comprised of three courses:
- The Total Data Quality Framework (viewing)
- Measuring Total Data Quality
- Design Strategies for Maximizing Total Data Quality
Good to know
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
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser