Design Strategies for Maximizing Total Data Quality from Coursera

By the end of this third course in the Total Data Quality Specialization, learners will be able to:

1. Learn about design tools and techniques for maximizing TDQ across all stages of the TDQ framework during a data collection or a data gathering process.

2. Identify aspects of the data generating or data gathering process that impact TDQ and be able to assess whether and how such aspects can be measured.

3. Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data.

4. Develop solutions to hypothetical design problems arising during the process of data collection or data gathering and processing.

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 and Maximizing Validity and Data Origin Quality

Welcome to Design Strategies for Maximizing Total Data Quality! This is the third and final course in the Total Data Quality Specialization. After viewing a short welcome video, reviewing the course syllabus, and taking a course pre-survey, we’ll begin the course by exploring the topic of validity. You’ll learn how to maximize validity for both designed and gathered data through a series of video lectures, readings, and case studies. We’ll conclude our exploration of validity with a quiz on design strategies for maximizing validity. The second half of Week 1 will focus on data origin. You’ll learn how to maximize data origin quality for designed and gathered data through a series of lectures, examples, and case studies. Week 1 will conclude with a quiz on design strategies for maximizing data origin quality.

Maximizing Processing and Data Access Quality

In Week 2, we’ll learn how to optimize data processing quality. We’ll begin the week with video lectures on how to maximize processing quality for designed and gathered data, along with an example for each type of data. We’ll conclude our discussion of processing with a quiz on design strategies for maximizing processing quality. Then, we’ll learn how to maximize data access quality for designed and gathered data while exploring each type of data through video examples and readings. Week 2 will conclude with a short quiz on strategies for maximizing access quality.

Maximizing Data Source Quality and Minimizing Data Missingness

This week, we’ll learn how to optimize the quality of a data source and minimize missing data rates. First, we’ll explore how to maximize data source quality for designed and gathered data. We’ll mix in a series of examples, readings, and case studies throughout our data source unit and conclude this unit with a quiz on strategies for maximizing source quality. Then, we’ll move on to a discussion of data missingness. We’ll learn how to minimize data missingness for designed and gathered data through a series of video lectures and examples. Week 3 will conclude with a short quiz on strategies for minimizing data missingness.

Maximizing the Quality of Data Analysis

Welcome to the final week of Design Strategies for Maximizing Total Data Quality and the Total Data Quality specialization! We’ll wrap up the series by learning how to optimize data analysis quality for both designed and gathered data. This exploration will include a series of video lectures and case studies. After you take a quiz on how to maximize data analysis quality, you’ll work on a peer review assignment that asks you to review a study of Wordle performance. The week will conclude with a specialization recap video and a course and specialization post-survey.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for learners who design and gather data to ensure its quality prior to analysis

Provides comprehensive instruction on maximizing data quality for both designed and gathered data

Covers essential concepts and strategies for maximizing total data quality, with a focus on validity, origin, access, and more

Led by experienced instructors Brady T. West, James Wagner, Jinseok Kim, and Trent D. Buskirk

Requires some background knowledge in data science, statistical analysis, and research methods

Lacks hands-on exercises or practical projects for applying the 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 Design Strategies for Maximizing Total Data Quality with these activities:

Compile a list of resources on Total Data Quality

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Creating a compilation of resources will provide a valuable reference for further learning and exploration.

Browse courses on Total Data Quality

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Search for articles, books, and websites on Total Data Quality
Review and select relevant resources
Organize and cite the resources in a structured format

Review materials from prerequisite courses (e.g. Statistics, Probability)

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A strong foundational understanding in the foundational concepts discussed in prerequisite courseware will make learning the concepts in this course much easier.

Browse courses on Statistics

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Review course notes and textbooks from Statistics and Probability
Complete practice problems and exercises from prerequisite textbooks

Refresh data collection principles

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Reviewing the principles of data collection will help establish a solid foundation for the course.

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Read textbook chapters on data collection
Review notes from previous courses on data collection
Complete practice exercises on data collection

14 other activities

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Review validity concepts

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Begin the course by reviewing general data science concepts, which is necessary for data quality assessments.

Browse courses on Validity

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Review definition of validity
Review different types of validity
Complete practice questions on validity assessment

Read 'Data Science for Business' by Provost and Fawcett

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This book provides a comprehensive overview of the practical applications of data science in a business context, which complements the theoretical concepts covered in the course.

View Data Science for Business: What You Need to... on Amazon

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Read the assigned chapters from 'Data Science for Business'
Take notes and highlight key concepts
Complete the chapter exercises and review questions

Learn about validity assessment techniques

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Understanding how to assess validity is crucial for designing data collection strategies that yield high-quality data.

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Watch video tutorials on validity assessment techniques
Read research papers on validity assessment
Practice applying validity assessment techniques to sample data

Identify and assess data collection methods

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Practice identifying and assessing different data collection methods to understand their impact on data quality.

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Review different data collection methods
Identify potential biases and errors associated with data collection methods
Complete practice exercises on data collection assessment

Complete online tutorials on data preprocessing techniques

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Data preprocessing is the cornerstone of data analysis, and this hands-on activity will provide you with experience using common techniques and tools.

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Identify a dataset that requires preprocessing
Choose and apply appropriate data cleaning techniques (e.g., handling missing values, removing outliers)
Explore different data transformation methods (e.g., scaling, normalization, feature engineering)

Participate in discussions on data quality challenges

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Engaging in discussions with peers can provide diverse perspectives and insights into data quality challenges.

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Join online forums or discussion boards
Contribute to discussions by sharing experiences and asking questions
Reflect on and synthesize the insights gained from discussions

Solve data quality assessment problems on Kaggle

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Kaggle offers a variety of data quality assessment problems that provide practical experience in identifying and addressing common data issues.

Browse courses on Data Quality

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Create a Kaggle account
Join the 'Data Quality Assessment' competition or notebook
Attempt to solve the data quality assessment problems provided

Join a study group to discuss course concepts and work on projects

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Engaging with peers promotes active learning and a deeper understanding of the material.

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Identify a study group or form a new one with classmates
Meet regularly to discuss assigned readings and work on course projects
Collaborate with peers to solve problems and share insights

Practice designing data collection plans

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Designing effective data collection plans requires practice and iteration. This activity provides opportunities to refine this skill.

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Identify research questions and objectives
Select appropriate data collection methods
Develop data collection instruments
Test and refine data collection plans

Design a data gathering strategy

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Create a data gathering strategy to apply the concepts learned in the course and enhance data quality.

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Define data collection objectives and scope
Identify potential data sources and assess their quality
Design data collection methods and procedures
Develop a data quality control plan
Present proposed data gathering strategy for feedback

Design a data collection instrument for a specific research question

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Creating a data collection instrument is a crucial step in the research process, as it ensures the quality and validity of the data you gather.

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Define the research question and objectives
Identify the target population and sampling strategy
Choose the appropriate data collection method (e.g., survey, interview, focus group)
Develop and pilot test the data collection instrument

Volunteer for a data collection project

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Practical experience in data collection provides hands-on learning and exposes students to real-world challenges.

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Identify organizations or research projects seeking volunteers
Apply for and secure a volunteer position
Participate in data collection activities
Reflect on the challenges and best practices encountered

Attend a workshop on data visualization best practices

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Visualizing data effectively is a valuable skill for communicating insights and understanding data patterns.

Browse courses on Data Visualization

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Identify a data visualization workshop or event
Attend the workshop and actively participate in the exercises
Apply the learned techniques to create your own data visualizations

Contribute to an open-source data quality project on GitHub

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Contributing to open source projects allows you to apply your skills, learn from others, and directly impact the advancement of data quality tools and techniques.

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Identify a data quality project on GitHub that interests you
Read the project documentation and identify potential areas for contribution
Create a pull request with your proposed changes or additions

Career center

Learners who complete Design Strategies for Maximizing Total Data Quality will develop knowledge and skills that may be useful to these careers:

Data Analyst

As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use their knowledge of data analysis and modeling to solve complex business problems. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers are responsible for building and maintaining the infrastructure that supports data analysis and modeling. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts use data to help businesses understand their customers and make better decisions. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to better business decisions.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers collect and analyze data to help businesses understand their customers and make better decisions. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to better market research insights.

See salaries and explore the career path for Market Researcher

UX Researcher

UX Researchers collect and analyze data to help businesses improve the user experience of their products and services. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to better UX research insights.

See salaries and explore the career path for UX Researcher

Survey Researcher

Survey Researchers collect and analyze data to help businesses understand their customers and make better decisions. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to better survey research insights.

See salaries and explore the career path for Survey Researcher

Data Journalist

Data Journalists use data to tell stories and inform the public. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to more accurate and informative journalism.

See salaries and explore the career path for Data Journalist

Policy Analyst

Policy Analysts use data to inform public policy. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to better policy decisions.

See salaries and explore the career path for Policy Analyst

Epidemiologist

Epidemiologists use data to investigate the causes and spread of disease. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to more accurate and informative epidemiological research.

See salaries and explore the career path for Epidemiologist

Statistician

Statisticians use data to make inferences about the world. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to more accurate and informative statistical analyses.

See salaries and explore the career path for Statistician

Quantitative Researcher

Quantitative Researchers use data to answer research questions. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to more accurate and informative research findings.

See salaries and explore the career path for Quantitative Researcher

Economist

Economists use data to analyze economic trends and make predictions. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can lead to more accurate and informative economic analyses.

See salaries and explore the career path for Economist

Data Archivist

Data Archivists are responsible for preserving and managing data for future use. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can help ensure the long-term integrity of the data.

See salaries and explore the career path for Data Archivist

Data Curator

Data Curators are responsible for managing and organizing data to make it accessible and usable for researchers and other users. This course can help you develop the skills you need to be successful in this role by teaching you how to design data collection and gathering processes that maximize data quality. You will also learn how to identify and mitigate potential sources of data error and bias, which can help ensure the accuracy and usability of the data.

See salaries and explore the career path for Data Curator