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Tricia Bagley
You have probably heard the expression “garbage in and garbage out.” When it comes to having confidence in a data set, “garbage in” refers having poor data quality. Poor data quality translates to poor quality or low confidence in the insights mined from the...
Read more
You have probably heard the expression “garbage in and garbage out.” When it comes to having confidence in a data set, “garbage in” refers having poor data quality. Poor data quality translates to poor quality or low confidence in the insights mined from the...
Read more
You have probably heard the expression “garbage in and garbage out.” When it comes to having confidence in a data set, “garbage in” refers having poor data quality. Poor data quality translates to poor quality or low confidence in the insights mined from the data. How do we shore up the data quality of a survey data set so we can have confidence in using that data for decision-making? We apply Exploratory Data Analysis or EDA methodology to identify strategies to handle and replace missing values.
In your Handle Missing Survey Data Values in Google Sheets project, you will gain hands-on experience conducting EDA, identifying strategies for handling missing values, and replacing missing values in a survey data set. To do this you will work in the free-to-use spreadsheet software Google Sheets.
By the end of this project, you will be able to confidently handle missing values in a survey data set to aid in shoring up the data quality and confidence in using the data for decision-making.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/7y2e2h/handle
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Helps learners explore how poor data quality impacts the insights mined from the data
Provides hands-on experience conducting Exploratory Data Analysis (EDA) to identify strategies for handling missing values
Utilizes Google Sheets, a free-to-use spreadsheet software, for practical application
Develops skills in handling missing values, a common challenge in survey data analysis
Designed for learners based in the North America region
May require learners to have background knowledge in data analysis and statistical concepts
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Reviews summary
Practical survey data handling
This course is a practical guide to handling missing survey data values in Google Sheets. It provides hands-on experience in identifying and replacing missing values, and helps learners gain confidence in their ability to clean and prepare data for analysis.
Easy to follow
"Thumbs Up"
Try it now!
"Good sharing! You must try it now!"
Hands-on experience
"gain hands-on experience conducting EDA"
"You will gain hands-on experience conducting EDA, identifying strategies for handling missing values, and replacing missing values in a survey data set."
Too easy
"too easy"
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 Handle Missing Survey Data Values in Google Sheets with these
activities:
Compile Google Sheets Resources
Show steps
Create a collection of resources to help you quickly find information while working with Google Sheets.
Browse courses on
Google Sheets
Show steps
-
Gather links to helpful articles and tutorials
-
Organize resources by topic
-
Create a shareable document to store your resources
Review Basic Statistics
Show steps
Review basic statistical concepts such as measures of central tendency, probability, and hypothesis testing to strengthen your foundation for this course.
Browse courses on
Statistics
Show steps
-
Read textbook chapters or online resources on basic statistics.
-
Practice solving problems involving mean, median, mode, and standard deviation.
-
Review concepts of probability distributions, including normal and binomial distributions.
-
Conduct hypothesis testing exercises using t-tests and chi-square tests.
Follow Online Tutorials on Handling Missing Values
Show steps
Seek additional guidance by exploring online tutorials that provide step-by-step instructions and explanations on how to effectively handle missing values.
Browse courses on
Missing Values
Show steps
-
Search for tutorials on platforms like Coursera, EdX, or YouTube.
-
Select tutorials that cover topics relevant to your course, such as missing value imputation and deletion.
-
Follow the instructions and apply the techniques to your own datasets.
-
Review the explanations and examples provided in the tutorials to deepen your understanding.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Practice Manipulating Data in Google Sheets
Show steps
Complete exercises to reinforce your understanding of how to manipulate data in Google Sheets.
Browse courses on
Data Manipulation
Show steps
-
Find practice exercises online
-
Complete exercises independently
-
Review your work and identify areas for improvement
Complete EDA Practice Problems
Show steps
Engage in hands-on practice to enhance your understanding of EDA techniques and their application in identifying strategies for handling missing values.
Browse courses on
Exploratory Data Analysis
Show steps
-
Find datasets with missing values online or create your own.
-
Apply EDA techniques to explore and visualize the data, identifying patterns and outliers.
-
Practice handling missing values using different methods (e.g., imputation, deletion).
-
Evaluate the impact of missing value handling techniques on the accuracy and reliability of the data.
Participate in Study Groups
Show steps
Engage with other students in study groups to discuss best practices for handling missing values, exchange insights, and receive feedback on your approaches.
Show steps
-
Find or form study groups with classmates.
-
Set regular meeting times and establish a study schedule.
-
Bring specific questions or challenges related to missing value handling.
-
Share your findings, techniques, and experiences with the group.
-
Provide and receive constructive feedback to improve understanding and approaches.
Develop a Data Cleaning and Analysis Plan
Show steps
Apply your knowledge of missing value handling techniques by creating a comprehensive plan that outlines how you will clean, analyze, and handle missing values in a specified dataset.
Show steps
-
Select a dataset with missing values.
-
Analyze the dataset to identify patterns and types of missing values.
-
Develop a strategy for handling missing values, including imputation techniques and deletion criteria.
-
Implement the plan and evaluate the impact on the data quality and analysis results.
-
Document the plan and findings in a written report.
Follow Tutorials on Advanced Google Sheets Techniques
Show steps
Enhance your Google Sheets skills by following tutorials that cover advanced techniques.
Browse courses on
Google Sheets
Show steps
-
Identify specific advanced techniques you want to learn
-
Search for and select high-quality tutorials
-
Follow the tutorials step-by-step
-
Practice applying the techniques in your own projects
Mentor Junior Students or Classmates
Show steps
Share your knowledge and skills by mentoring junior students or classmates who may be struggling with concepts related to missing value handling.
Show steps
-
Identify junior students or classmates who could benefit from your guidance.
-
Offer to provide support and assistance in understanding missing value handling techniques.
-
Regularly meet with them to provide guidance, answer questions, and review their work.
-
Provide constructive feedback and encouragement to help them improve their understanding.
-
Assess their progress and adjust your mentoring approach as needed.
Contribute to Open-Source Data Science Projects
Show steps
Engage with the open-source community by contributing to projects related to data science and missing value handling, enhancing your practical skills and knowledge.
Browse courses on
Data Science Projects
Show steps
-
Identify open-source projects on platforms like GitHub that focus on data science and data cleaning.
-
Review the project documentation and codebase to understand the project's goals and requirements.
-
Identify areas where you can contribute your skills, such as implementing new missing value handling algorithms or improving existing ones.
-
Make code contributions, submit feature requests, or report bugs to the project repository.
-
Engage with the project maintainers and community to discuss your contributions and learn from others.
Create a Data Visualization Dashboard in Google Sheets
Show steps
Apply your knowledge of Google Sheets to create an interactive data visualization dashboard.
Browse courses on
Data Visualization
Show steps
-
Determine the purpose and audience of your dashboard
-
Gather and prepare your data
-
Select appropriate charts and graphs
-
Design and layout your dashboard
-
Share and present your dashboard
Compile Google Sheets Resources
Show steps
Create a collection of resources to help you quickly find information while working with Google Sheets.
Browse courses on
Google Sheets
Show steps
Show steps
Show steps
- Gather links to helpful articles and tutorials
- Organize resources by topic
- Create a shareable document to store your resources
Review Basic Statistics
Show steps
Review basic statistical concepts such as measures of central tendency, probability, and hypothesis testing to strengthen your foundation for this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Read textbook chapters or online resources on basic statistics.
- Practice solving problems involving mean, median, mode, and standard deviation.
- Review concepts of probability distributions, including normal and binomial distributions.
- Conduct hypothesis testing exercises using t-tests and chi-square tests.
Follow Online Tutorials on Handling Missing Values
Show steps
Seek additional guidance by exploring online tutorials that provide step-by-step instructions and explanations on how to effectively handle missing values.
Browse courses on
Missing Values
Show steps
Show steps
Show steps
- Search for tutorials on platforms like Coursera, EdX, or YouTube.
- Select tutorials that cover topics relevant to your course, such as missing value imputation and deletion.
- Follow the instructions and apply the techniques to your own datasets.
- Review the explanations and examples provided in the tutorials to deepen your understanding.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Practice Manipulating Data in Google Sheets
Show steps
Complete exercises to reinforce your understanding of how to manipulate data in Google Sheets.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Find practice exercises online
- Complete exercises independently
- Review your work and identify areas for improvement
Complete EDA Practice Problems
Show steps
Engage in hands-on practice to enhance your understanding of EDA techniques and their application in identifying strategies for handling missing values.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Find datasets with missing values online or create your own.
- Apply EDA techniques to explore and visualize the data, identifying patterns and outliers.
- Practice handling missing values using different methods (e.g., imputation, deletion).
- Evaluate the impact of missing value handling techniques on the accuracy and reliability of the data.
Participate in Study Groups
Show steps
Engage with other students in study groups to discuss best practices for handling missing values, exchange insights, and receive feedback on your approaches.
Show steps
Show steps
Show steps
- Find or form study groups with classmates.
- Set regular meeting times and establish a study schedule.
- Bring specific questions or challenges related to missing value handling.
- Share your findings, techniques, and experiences with the group.
- Provide and receive constructive feedback to improve understanding and approaches.
Develop a Data Cleaning and Analysis Plan
Show steps
Apply your knowledge of missing value handling techniques by creating a comprehensive plan that outlines how you will clean, analyze, and handle missing values in a specified dataset.
Show steps
Show steps
Show steps
- Select a dataset with missing values.
- Analyze the dataset to identify patterns and types of missing values.
- Develop a strategy for handling missing values, including imputation techniques and deletion criteria.
- Implement the plan and evaluate the impact on the data quality and analysis results.
- Document the plan and findings in a written report.
Follow Tutorials on Advanced Google Sheets Techniques
Show steps
Enhance your Google Sheets skills by following tutorials that cover advanced techniques.
Browse courses on
Google Sheets
Show steps
Show steps
Show steps
- Identify specific advanced techniques you want to learn
- Search for and select high-quality tutorials
- Follow the tutorials step-by-step
- Practice applying the techniques in your own projects
Mentor Junior Students or Classmates
Show steps
Share your knowledge and skills by mentoring junior students or classmates who may be struggling with concepts related to missing value handling.
Show steps
Show steps
Show steps
- Identify junior students or classmates who could benefit from your guidance.
- Offer to provide support and assistance in understanding missing value handling techniques.
- Regularly meet with them to provide guidance, answer questions, and review their work.
- Provide constructive feedback and encouragement to help them improve their understanding.
- Assess their progress and adjust your mentoring approach as needed.
Contribute to Open-Source Data Science Projects
Show steps
Engage with the open-source community by contributing to projects related to data science and missing value handling, enhancing your practical skills and knowledge.
Browse courses on
Data Science Projects
Show steps
Show steps
Show steps
- Identify open-source projects on platforms like GitHub that focus on data science and data cleaning.
- Review the project documentation and codebase to understand the project's goals and requirements.
- Identify areas where you can contribute your skills, such as implementing new missing value handling algorithms or improving existing ones.
- Make code contributions, submit feature requests, or report bugs to the project repository.
- Engage with the project maintainers and community to discuss your contributions and learn from others.
Create a Data Visualization Dashboard in Google Sheets
Show steps
Apply your knowledge of Google Sheets to create an interactive data visualization dashboard.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Determine the purpose and audience of your dashboard
- Gather and prepare your data
- Select appropriate charts and graphs
- Design and layout your dashboard
- Share and present your dashboard
Career center
Learners who complete Handle Missing Survey Data Values in Google Sheets will develop knowledge and skills
that may be useful to these careers:
Business Analyst
Business Analyst
As a Business Analyst, you will be responsible for analyzing data to help businesses improve their operations. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Business Analyst. By taking this course, you will be able to improve the quality of your data analysis and make better recommendations to your clients.
Market Researcher
Market Researcher
As a Market Researcher, you will be responsible for collecting and analyzing data to help businesses understand their customers. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Market Researcher. By taking this course, you will be able to improve the quality of your market research and make better recommendations to your clients.
Data Scientist
Data Scientist
As a Data Scientist, you will use your skills in data analysis and machine learning to solve real-world problems. This course will teach you how to handle missing data values in Google Sheets, which is an important skill for any Data Scientist. By taking this course, you will be able to improve the accuracy of your machine learning models and make better predictions.
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Data Analyst. By taking this course, you will be able to improve the quality of your data analysis and make more informed decisions.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data to help businesses make better decisions. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Financial Analyst. By taking this course, you will be able to improve the quality of your financial analysis and make better recommendations to your clients.
Data Engineer
Data Engineer
As a Data Engineer, you will be responsible for building and maintaining data pipelines. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Data Engineer. By taking this course, you will be able to improve the quality of your data pipelines and ensure that your data is ready for analysis.
Data Visualization Analyst
Data Visualization Analyst
As a Data Visualization Analyst, you will be responsible for creating data visualizations to help businesses understand their data. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Data Visualization Analyst. By taking this course, you will be able to improve the quality of your data visualizations and ensure that they are clear and informative.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for developing and maintaining software applications. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Software Engineer. By taking this course, you will be able to improve the quality of your software applications and ensure that they are reliable and efficient.
Product Manager
Product Manager
As a Product Manager, you will be responsible for planning and developing new products. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Product Manager. By taking this course, you will be able to improve the quality of your product planning and development and ensure that your products meet the needs of your customers.
Business Intelligence Analyst
Business Intelligence Analyst
As a Business Intelligence Analyst, you will be responsible for analyzing data to help businesses make better decisions. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Business Intelligence Analyst. By taking this course, you will be able to improve the quality of your data analysis and make better recommendations to your clients.
Project Manager
Project Manager
As a Project Manager, you will be responsible for planning and executing projects. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Project Manager. By taking this course, you will be able to improve the quality of your project planning and execution and ensure that your projects are successful.
Epidemiologist
Epidemiologist
As an Epidemiologist, you will be responsible for investigating the causes of disease and promoting public health. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Epidemiologist. By taking this course, you will be able to improve the quality of your epidemiological research and make better recommendations for public health policy.
Statistician
Statistician
As a Statistician, you will be responsible for collecting, analyzing, and interpreting data. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Statistician. By taking this course, you will be able to improve the quality of your data analysis and make better inferences.
Actuary
Actuary
As an Actuary, you will be responsible for assessing and managing risk. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Actuary. By taking this course, you will be able to improve the quality of your risk assessment and make better recommendations for your clients.
Survey Researcher
Survey Researcher
As a Survey Researcher, you will be responsible for designing and conducting surveys to collect data. This course will teach you how to handle missing data values in Google Sheets, which is a valuable skill for any Survey Researcher. By taking this course, you will be able to improve the quality of your surveys and collect more accurate data.
For more career information including salaries, visit:
OpenCourser.com/course/7y2e2h/handle
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
Handle Missing Survey Data Values in Google Sheets .
Save
Provides a comprehensive introduction to deep learning. It valuable resource for anyone who wants to learn more about deep learning in general or for those who are looking for a more technical introduction to the field.
Provides a comprehensive guide to machine learning with Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn more about machine learning in general or for those who are looking for a more technical introduction to the field.
Provides a comprehensive guide to using Google Sheets for data analysis. It covers a wide range of topics, including data cleaning, data manipulation, and data visualization. It valuable resource for anyone who wants to learn more about using Google Sheets for data analysis.
Provides a comprehensive guide to data visualization with Python. It covers a wide range of topics, including data visualization techniques, data visualization tools, and data visualization best practices. It valuable resource for anyone who wants to learn more about using Python for data visualization.
Save
Practical guide to exploratory data analysis using R. It covers a wide range of topics, including data visualization, data transformation, and statistical modeling. It valuable resource for anyone who wants to learn more about EDA.
Provides a comprehensive introduction to natural language processing with Python. It valuable resource for anyone who wants to learn more about natural language processing in general or for those who are looking for a more technical introduction to the field.
Provides a comprehensive overview of data science, including data mining and data-analytic thinking. It valuable resource for anyone who wants to learn more about these topics.
Provides a gentle introduction to data science. It valuable resource for anyone who wants to learn more about data science in general or for those who are looking for a more non-technical introduction to the field.
Provides a gentle introduction to Python programming. It valuable resource for anyone who wants to learn more about programming in general or Python in particular.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7y2e2h/handle
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Tricia Bagley
Good to know
Helps learners explore how poor data quality impacts the insights mined from the data
Provides hands-on experience conducting Exploratory Data Analysis (EDA) to identify strategies for handling missing values
Utilizes Google Sheets, a free-to-use spreadsheet software, for practical application
Develops skills in handling missing values, a common challenge in survey data analysis
Designed for learners based in the North America region
May require learners to have background knowledge in data analysis and statistical concepts
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