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Software Engineering

What is Data Cleaning?

James Gallagher - January 04, 2021


When data scientists talk about “cleaning” data, it’s hard to interpret them literally. This is reasonable because data scientists do not physically clean data with sanitizer. Cleaning data involves making a dataset useful by removing and modifying erroneous or irrelevant values.

In this guide, we’re going to discuss what data cleaning is, why it is important, and how data scientists clean data.

What is Data Cleaning?

Data cleaning is when a programmer removes incorrect and duplicate values from a dataset and ensures that all values are formatted in the way they want. Data cleaning is sometimes called data scrubbing because it involves cleaning “dirty data”.

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Rarely does raw data come in a neatly-packaged file that accounts for everything you need to do with the dataset. That’s where cleaning comes in.

When a data scientist receives a dataset, the first task they have to undertake is data cleaning. They need to spend time reading over a data set to make sure that they can use it in their program.

Data cleaning is a good opportunity for a data scientist to become familiar with a dataset. By cleaning a dataset, a data scientist learns more about what data is included in a dataset, how it is formatted, and what data they do not have available.

Why is Data Cleaning Important?

Data cleaning helps people who work in data science improve the accuracy of their conclusions. The goal of a data scientist is to find the answers to questions using data. If a data scientist is working with bad data, then their conclusion is less likely to be accurate.

What’s more, data cleaning helps save time further down the line. Data cleaning comes before analysis. This means that by the time a data scientist analyzes data, and way before they make any conclusions, their dataset will be prepared in exactly the way they want.

Having a clean dataset means a data scientist can progress through an analysis knowing they will not have to go back and correct improperly formatted or remove inaccurate values.

Ultimately, a data scientist wants their dataset to make sense and include all of the data they need to draw an informed conclusion to a question.

How Do You Clean Data?

Every data scientist follows their own procedure for cleaning data. Many organizations have their own standard guidelines to make sure a dataset has gone through rigorous cleaning before it is used in any data analysis .

There are a few common processes in all data cleaning reviews.

Review Missing Data

Data scientists want all of the data they need to conduct an analysis on to be ready before they start. That’s why a data scientist will review any missing data during the cleaning process.

If data is not available in a dataset, a data scientist may choose to alter their plan so that they do not rely on that data. This has to be carefully considered because it may change the final conclusions that the data scientist is able to make.

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A data scientist may decide to calculate missing values based on existing data. For instance, if a data scientist needs an average of numbers, they can calculate that using a program. They don’t need to remove any analysis dependent on an average from their analysis.

A data scientist may also add in values like 0 or null to make sure a data set can be readily processed by a program. These values will replace empty gaps in a dataset which may cause structural errors.

Remove Useless Data

Some data that comes in a data set will add no value to a dataset. While having more data can be useful, some data points can distract a data scientist during their analysis.

Before analysis begins using data analytics tools , a data scientist will remove all of the data that is irrelevant to their studies. This will reduce the size of their data set, thereby making the data set easier to work with. The more data points an analyst has to think about, the more likely they are to introduce unnecessary complexity into their study.

Delete Duplicate Data

When a dataset is gathered, there is a chance duplicate entries will make their way into the set. This can happen if a dataset was not validated when it was collected or if multiple datasets are being combined which have overlapping data points.

Removing duplicate data ensures that the conclusions drawn are based on the right values. If duplicate data were to exist in a dataset, the data may skew toward one conclusion over another. This would significantly impact the accuracy of the final conclusions.

Process Outlier Data

A dataset may contain outlier values. For instance, there may be one single value that is empty, or a record that is corrupted. A data scientist will look at a dataset and make sure there are no outlier values.

If there are outlier values, there are two courses of action. A data scientist may choose to remove the outlier entirely from the dataset. This is likely if an outlier value has a low chance of being accurate.

A data scientist may also decide to double-check a value. This allows a data scientist to check for mistakes in data entry or collection before excluding a value.

Conclusion

Data cleaning is a fundamental part of the data analysis process. Cleaning happens after data is collected and before analysis. During the cleaning process, a data scientist will work to ensure that a dataset is valid, accurate, and includes all the values they need.

Without data cleaning, data scientists would have to go back-and-forth between analyzing a dataset and fixing issues with the underlying data. This is likely to confuse the data analysis process to the point where the final conclusion loses its accuracy.

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James Gallagher

About the author: James Gallagher is a self-taught programmer and the technical content manager at Career Karma. He has experience in range of programming languages and extensive expertise in Python, HTML, CSS, and JavaScript. James has written hundreds of programming tutorials, and he frequently contributes to publications like Codecademy, Treehouse, Repl.it, Afrotech, and others. He also serves as a researcher at Career Karma, publishing comprehensive reports on the bootcamp market.

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