Handle Missing Survey Data Values in Google Sheets

Offered By
Coursera Project Network
In this Guided Project, you will:

Understand the value of handling missing values when preparing data for analysis.

Consider best practices for removing data entries in survey data sets and impute missing values with methods of centrality in Google Sheets.

Consider when survey values can be restored from other sources and impute values with cross-validation methods in Google Sheets.

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Survey MethodologyStatistical Data PreparationImpute Missing ValuesBusiness IntelligenceData Validation

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Review Exploratory Data Analysis (EDA) and how it is aids identifying missing values in a data set.

  2. Examine the handling of missing values in data preparation.

  3. Import data, identify missing values with a chart, and build a framework to handle them.

  4. Review when to remove data entries and impute missing values with methods of centrality.

  5. Impute survey data values through cross-validation.

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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