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Brady T. West,
James Wagner,
Jinseok Kim,
and
Trent D Buskirk
By the end of this second course in the Total Data Quality Specialization, learners will be able to:
1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework.
2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source.
3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study.
4. Identify relevant software and related tools for computing the various metrics.
Read more
By the end of this second course in the Total Data Quality Specialization, learners will be able to:
1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework.
2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source.
3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study.
4. Identify relevant software and related tools for computing the various metrics.
Read more
By the end of this second course in the Total Data Quality Specialization, learners will be able to:
1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework.
2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source.
3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study.
4. Identify relevant software and related tools for computing the various metrics.
5. Understand metrics that can be computed for both designed and found/organic data.
6. Apply the metrics to real data and interpret their resulting values from a TDQ perspective.
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/8r5za3/measuring
What's inside
Syllabus
Introduction and Measuring Validity and Data Origin Quality
Welcome to Measuring Total Data Quality! This is the second course in the Total Data Quality Specialization. After reviewing the Course 2 syllabus and completing the course pre-survey, you’ll learn how to measure validity for designed and gathered data through a series of video lectures, examples, and readings. You’ll then take a short quiz on interpreting validity metrics. Then, you’ll complete a module on data origin, where you’ll learn about measuring data origin quality for designed and gathered data in a series of video lectures and case studies. Week 1 will conclude with a quiz on interpreting data origin quality metrics.
Read more
Syllabus
Introduction and Measuring Validity and Data Origin Quality
Welcome to Measuring Total Data Quality! This is the second course in the Total Data Quality Specialization. After reviewing the Course 2 syllabus and completing the course pre-survey, you’ll learn how to measure validity for designed and gathered data through a series of video lectures, examples, and readings. You’ll then take a short quiz on interpreting validity metrics. Then, you’ll complete a module on data origin, where you’ll learn about measuring data origin quality for designed and gathered data in a series of video lectures and case studies. Week 1 will conclude with a quiz on interpreting data origin quality metrics.
Read more
Measuring Processing and Data Access Quality
Welcome to Week 2 of Measuring Total Data Quality! We’ll begin the week by discussing how to measure processing data quality for designed and gathered data. We’ll include examples of measuring process data quality for each form of data and conclude the module with a quiz on interpreting processing metrics. In the second half of Week 2, we’ll discuss measuring data access quality for designed and gathered data through video lectures, an example, and a case study, and conclude the week with a quiz on interpreting access metrics.
Measuring Data Source Quality and Data Missingness
This week, we’ll learn how to measure data source quality and data missingness. We’ll begin Week 3 with a video lecture on measuring data source quality for designed data. Then, we’ll work through an example of computing data source metrics with real data and code. We’ll then learn how to measure data source quality for gathered data and see an example of computer data source quality metrics with real data and code. You’ll then take a short quiz on interpreting data source quality metrics and move on to the Data Missingness unit. We’ll learn how to measure threats to data source quality for designed and gathered data and work through examples for each form of data. Week 3 will conclude with a quiz on interpreting data missingness metrics.
Measuring the Quality of Data Analysis
We’ll be wrapping up Measuring Total Data Quality this week by learning how to measure the quality of data analysis. We’ll learn how to measure the quality of data analysis for designed and gathered data and work through examples of each type of data. We recommend that you complete two readings before you complete the lecture on measuring the quality of analysis for gathered data. We will conclude the week with a quiz on examining quality metrics and interpreting output, as well as references for the Measuring Total Data Quality course and a course post-survey.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in quantifying overall data quality critical for all data science and quantitative analyst projects
This course strengthens an existing foundational understanding of data collection and data science
Provides a comprehensive examination of tools and methods for evaluating data quality
Builds a strong foundation for beginners in the field of data quality
Teaches techniques for maximizing data quality before conducting analysis
Taught by Brady West, James Wagner, Jinseok Kim, and Trent Buskirk
Save this course
Save Measuring Total Data Quality to your list so you can find it easily later:
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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 Measuring Total Data Quality with these
activities:
Complete the Data Quality Online Primer
Show steps
Refreshes your knowledge of data quality concepts before starting the course, filling any gaps in understanding.
Browse courses on
Data Quality
Show steps
-
Read Data Quality and Data Quality Dimensions
-
Enroll in a free Data Quality Online Primer course on Coursera
Intro to Data Quality Concepts
Show steps
This will provide the foundational knowledge necessary to execute the techniques discussed in the course.
Browse courses on
Data Quality
Show steps
-
Review data quality definitions and concepts.
-
Identify the different dimensions of data quality.
-
Discuss the importance of data quality in data analysis.
Review design and data quality metrics
Show steps
Brush up on data quality metrics before starting the course to refresh your mind for greater clarity and understanding during the classes.
Show steps
-
Read the TDQ Framework Reference Guide
-
Complete the TDQ Self-Assessment Tool
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Review Data Structures and Algorithms
Show steps
This activity will help you refresh your fundamental skills in data structures and algorithms to better prepare yourself for the content of this course.
Browse courses on
Data Structures
Show steps
-
Review common data structure implementations such as arrays, linked lists, stacks, and queues.
-
Practice basic algorithms for searching, sorting, and traversal.
-
Complete coding exercises on platforms like LeetCode or HackerRank.
Create a Data Quality Plan
Show steps
Develop a plan to ensure data quality throughout the course projects, improving the reliability and consistency of your data analysis.
Show steps
-
Define data quality objectives
-
Identify data sources and collection methods
-
Establish data quality standards and metrics
-
Consider data management and storage strategies
Data Quality Assessment using OpenRefine
Show steps
Build familiarity with tools and techniques for data quality assessment.
Browse courses on
Data Quality Assessment
Show steps
-
Find and install OpenRefine.
-
Load a dataset into OpenRefine.
-
Explore the data and identify potential quality issues.
-
Use OpenRefine's built-in tools to clean and transform the data.
-
Export the cleaned data to a new file.
Practice Data Quality Calculations
Show steps
Sharpen your skills in calculating and interpreting data quality metrics, improving your ability to assess data quality.
Show steps
-
Calculate Data Accuracy, Completeness, Consistency, and Validity
-
Use data quality tools to automate calculations
Data Quality Metrics Calculation
Show steps
Increase proficiency in calculating and interpreting data quality metrics.
Show steps
-
Learn the formulas for calculating common data quality metrics, such as completeness, accuracy, and consistency.
-
Practice calculating these metrics using real-world datasets.
-
Interpret the results of your calculations and identify potential data quality issues.
Data Quality Best Practices Guide
Show steps
Solidify understanding of data quality principles by creating a comprehensive resource.
Show steps
-
Research and gather information on data quality best practices.
-
Organize the information into a logical structure.
-
Write clear and concise instructions on how to implement these best practices.
-
Share the guide with others.
Kaggle Data Quality Challenge
Show steps
Gain practical experience in applying data quality techniques and showcase your skills.
Browse courses on
Kaggle
Show steps
-
Find and register for a Kaggle competition focused on data quality.
-
Download the competition dataset and explore it.
-
Develop and implement a data quality pipeline to clean and transform the data.
-
Submit your results to the competition and compare them to other participants.
Data Quality Report for a Real-World Dataset
Show steps
Demonstrate mastery of data quality concepts through a comprehensive analysis.
Show steps
-
Choose a real-world dataset that is relevant to your interests or work.
-
Assess the quality of the dataset using the techniques covered in the course.
-
Write a report that summarizes your findings and provides recommendations for improving the data quality.
Complete the Data Quality Online Primer
Show steps
Refreshes your knowledge of data quality concepts before starting the course, filling any gaps in understanding.
Browse courses on
Data Quality
Show steps
Show steps
Show steps
- Read Data Quality and Data Quality Dimensions
- Enroll in a free Data Quality Online Primer course on Coursera
Intro to Data Quality Concepts
Show steps
This will provide the foundational knowledge necessary to execute the techniques discussed in the course.
Browse courses on
Data Quality
Show steps
Show steps
Show steps
- Review data quality definitions and concepts.
- Identify the different dimensions of data quality.
- Discuss the importance of data quality in data analysis.
Review design and data quality metrics
Show steps
Brush up on data quality metrics before starting the course to refresh your mind for greater clarity and understanding during the classes.
Show steps
Show steps
Show steps
- Read the TDQ Framework Reference Guide
- Complete the TDQ Self-Assessment Tool
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Review Data Structures and Algorithms
Show steps
This activity will help you refresh your fundamental skills in data structures and algorithms to better prepare yourself for the content of this course.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Review common data structure implementations such as arrays, linked lists, stacks, and queues.
- Practice basic algorithms for searching, sorting, and traversal.
- Complete coding exercises on platforms like LeetCode or HackerRank.
Create a Data Quality Plan
Show steps
Develop a plan to ensure data quality throughout the course projects, improving the reliability and consistency of your data analysis.
Show steps
Show steps
Show steps
- Define data quality objectives
- Identify data sources and collection methods
- Establish data quality standards and metrics
- Consider data management and storage strategies
Data Quality Assessment using OpenRefine
Show steps
Build familiarity with tools and techniques for data quality assessment.
Browse courses on
Data Quality Assessment
Show steps
Show steps
Show steps
- Find and install OpenRefine.
- Load a dataset into OpenRefine.
- Explore the data and identify potential quality issues.
- Use OpenRefine's built-in tools to clean and transform the data.
- Export the cleaned data to a new file.
Practice Data Quality Calculations
Show steps
Sharpen your skills in calculating and interpreting data quality metrics, improving your ability to assess data quality.
Show steps
Show steps
Show steps
- Calculate Data Accuracy, Completeness, Consistency, and Validity
- Use data quality tools to automate calculations
Data Quality Metrics Calculation
Show steps
Increase proficiency in calculating and interpreting data quality metrics.
Show steps
Show steps
Show steps
- Learn the formulas for calculating common data quality metrics, such as completeness, accuracy, and consistency.
- Practice calculating these metrics using real-world datasets.
- Interpret the results of your calculations and identify potential data quality issues.
Data Quality Best Practices Guide
Show steps
Solidify understanding of data quality principles by creating a comprehensive resource.
Show steps
Show steps
Show steps
- Research and gather information on data quality best practices.
- Organize the information into a logical structure.
- Write clear and concise instructions on how to implement these best practices.
- Share the guide with others.
Kaggle Data Quality Challenge
Show steps
Gain practical experience in applying data quality techniques and showcase your skills.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find and register for a Kaggle competition focused on data quality.
- Download the competition dataset and explore it.
- Develop and implement a data quality pipeline to clean and transform the data.
- Submit your results to the competition and compare them to other participants.
Data Quality Report for a Real-World Dataset
Show steps
Demonstrate mastery of data quality concepts through a comprehensive analysis.
Show steps
Show steps
Show steps
- Choose a real-world dataset that is relevant to your interests or work.
- Assess the quality of the dataset using the techniques covered in the course.
- Write a report that summarizes your findings and provides recommendations for improving the data quality.
Career center
Learners who complete Measuring Total Data Quality will develop knowledge and skills
that may be useful to these careers:
Data Quality Analyst
Data Quality Analyst
A Data Quality Analyst is responsible for ensuring that data is accurate, reliable, and consistent. They develop and implement data quality standards and procedures, and monitor data quality to identify and resolve issues. This course can be useful for aspiring Data Quality Analysts as it will provide them with the skills and knowledge needed to effectively evaluate and improve data quality.
Data Analyst
Data Analyst
A Data Analyst is responsible for transforming raw data into actionable insights that can help organizations make informed decisions. They help identify and remove errors in datasets. They gather data and ensure that data from different sources is valid and accurate. Measuring Total Data Quality is an excellent course for aspiring Data Analysts as it will provide them with the essential understanding of different metrics for evaluating Total Data Quality, and the skills to identify and address data quality issues.
Data Scientist
Data Scientist
Data Scientists develop and implement mathematical and statistical models to extract insights from data. They are responsible for ensuring that the data they use is accurate and reliable. This course is an excellent choice for aspiring Data Scientists as it will provide them with the skills and knowledge needed to assess the quality of data and identify and address data quality issues.
Quality Assurance Analyst
Quality Assurance Analyst
A Quality Assurance Analyst is responsible for testing and verifying the quality of products and services. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Quality Assurance Analysts as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Data Engineer
Data Engineer
A Data Engineer designs and builds systems to store, manage, and process data. They are responsible for ensuring that data is accurate, reliable, and accessible to data analysts and other users. The Measuring Total Data Quality course can be useful for aspiring Data Engineers as it will help them understand the importance of data quality and the different methods for evaluating and improving data quality.
Information Systems Manager
Information Systems Manager
An Information Systems Manager is responsible for planning, implementing, and maintaining information systems. They are responsible for ensuring that data is accurate, reliable, and accessible to users. The Measuring Total Data Quality course can be useful for aspiring Information Systems Managers as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to improve the efficiency and effectiveness of organizations. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Operations Research Analysts as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to understand customer needs and preferences. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Market Researchers as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Systems Analyst
Systems Analyst
A Systems Analyst designs and develops computer systems. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Systems Analysts as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Information Architect
Information Architect
An Information Architect designs and organizes information systems to ensure that they are easy to use and meet the needs of users. They work closely with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Information Architects as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Knowledge Manager
Knowledge Manager
A Knowledge Manager is responsible for creating and managing knowledge within an organization. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Knowledge Managers as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Software Engineers as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks to an organization. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Risk Analysts as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Product Manager
Product Manager
A Product Manager is responsible for the development and management of products. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Product Managers as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
Library Scientist
Library Scientist
A Library Scientist is responsible for organizing and managing information resources. They work with data analysts and other stakeholders to identify and address data quality issues. This course can be useful for aspiring Library Scientists as it will provide them with the skills and knowledge needed to understand and evaluate data quality.
For more career information including salaries, visit:
OpenCourser.com/course/8r5za3/measuring
Reading list
We've selected nine 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
Measuring Total Data Quality.
Provides a comprehensive framework for understanding and implementing Total Data Quality (TDQ). It covers all stages of the TDQ lifecycle, from data collection and integration to analysis and reporting.
Practical guide to data quality management. It covers a wide range of topics, including data quality assessment, data cleansing, and data governance.
Provides a detailed overview of data quality metrics and measurement techniques. It also includes case studies and examples of how to implement data quality improvement programs.
Focuses on the importance of data quality for data analytics. It covers topics such as data cleansing, data integration, and data governance.
Comprehensive guide to data quality management. It covers a wide range of topics, including data quality assessment, data cleansing, and data governance.
Focuses on the importance of data quality for business intelligence. It covers topics such as data quality assessment, data cleansing, and data governance.
Provides a practical guide to data quality management. It covers a wide range of topics, including data quality assessment, data cleansing, and data governance.
Provides a comprehensive overview of data quality principles and practices. It covers a wide range of topics, including data quality assessment, data cleansing, and data governance.
Primer on data quality. It covers a wide range of topics, including data quality assessment, data cleansing, and data governance.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8r5za3/measuring
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Effort
4 weeks of study, 1.5-3.5 hours/week
Level
Introductory
Via
Coursera
Institution
University of Michigan
Instructors
Brady T. West
James Wagner
Jinseok Kim
Trent D Buskirk
Language
English
This course belongs to a
collection
called
Total Data Quality. It's comprised of three courses:
- The Total Data Quality Framework
- Measuring Total Data Quality (viewing)
- Design Strategies for Maximizing Total Data Quality
Good to know
Develops skills in quantifying overall data quality critical for all data science and quantitative analyst projects
This course strengthens an existing foundational understanding of data collection and data science
Provides a comprehensive examination of tools and methods for evaluating data quality
Builds a strong foundation for beginners in the field of data quality
Teaches techniques for maximizing data quality before conducting analysis
Taught by Brady West, James Wagner, Jinseok Kim, and Trent Buskirk
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