Before you begin analyzing your data, it is crucial to ensure that your data set is complete and correct. This guide shares simple, yet crucial, techniques to help you clean your data effectively.
There are eight chapters in this ebook, each covering a distinct aspect of data cleaning.
Most data sets have instances of missing data. Incomplete or missing data points, no matter how few, can reduce your sample size (the number of people or entities in your data set) considerably.
Use this chapter to learn how to deal with missing data to ensure the best balance between data accuracy and minimal loss in sample size.
Understanding how to structure your data into rows and columns is a crucial first step in cleaning any data set.
A handy checklist of basic data checks to help you rule out some obvious errors in your data.
Learn about common question types and how to ensure data consistency for your survey responses.
Learn how to appropriately deal with missing data to ensure the best balance between data accuracy and minimal loss in sample size.
Outliers can be accurate or inaccurate. Learn different ways to detect outliers and deal with inaccurate data better.
Conditional questions may add complexity to your data. Learn how to deal with such questions in your data cleaning processes.
Before analyzing your data, check out these functions will help you make better sense of your data and draw better insights
This case study will help you understand the challenges involved and the basic steps followed in cleaning data from this paper-based survey.