University of Michigan
The Total Data Quality Framework
University of Michigan

The Total Data Quality Framework

This course is part of Total Data Quality Specialization

Taught in English

Some content may not be translated

Brady T. West
James Wagner
Jinseok Kim

Instructors: Brady T. West

2,054 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.5

(14 reviews)

Beginner level
No prior experience required
11 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify the essential differences between designed and gathered data.

  • Summarize the key dimensions of the Total Data Quality (TDQ) Framework.

  • Describe why data analysis defines an important dimension of the Total Data Quality framework.

  • Define the three measurement dimensions of the Total Data Quality framework.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(14 reviews)

Beginner level
No prior experience required
11 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Total Data Quality Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 4 modules in this course

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.

What's included

9 videos5 readings1 quiz

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.

What's included

13 videos5 readings3 quizzes

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.

What's included

16 videos3 readings2 quizzes

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.

What's included

5 videos6 readings1 quiz

Instructors

Brady T. West
University of Michigan
6 Courses148,877 learners

Offered by

Recommended if you're interested in Data Analysis

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions