The Data Science Skill They Don’t Talk About: Data Cleaning – Learnxt

The Data Science Skill They Don’t Talk About: Data Cleaning

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The Data Science Skill They Don't Talk About: Data Cleaning

Bad Data in is Terrible Analysis out!
If you are making imperative decisions in your firm based on quality data, then data cleaning is one of the most critical steps. Data cleaning is also known as Data Scrubbing or Data Cleansing.
What is Data Cleaning?
The process of fixing or removing corrupted, duplicate, incorrect, incomplete, or incomplete formatted data within a data set is known as data cleaning.
There are many opportunities for the data to get duplicated or mislabelled when multiple data sources are being combined. Even though they may look correct, the outcomes and analysis can get impacted if the data is incorrect. The process of data cleaning varies for each data set. Thus there is no prominent data scrubbing process. But to do the process right, it is crucial to have a defined template for data cleaning.
What are the techniques of Data Cleaning?
As discussed above, the techniques to clean data will vary for each data set. Still, you can follow the necessary steps given below to map data for your business.
Removing duplicate or irrelevant observations:
Including all the duplicate and irrelevant observations, remove all the unwanted observations from your data set. During data collection from multiple sources or clients, or while scraping data, there are high chances of creation of duplicate data and observations. One of the main areas of data cleaning is removing duplicate data.
Irrelevant observations can be classified as the observations that do not fit the data set you are reviewing. For example, data of the old age generation will be irrelevant if you are analysing data related to millennials. Removing irrelevant data can help you avoid any sort of distractions from your target and creating a manageable data set.
Fixing structural errors:
While transferring the data, we come across inevitable strange typos, naming conventions, or incorrect capitalisation, which are termed as structural errors. These structural errors cause inconsistent classes and mislabelled categories.
Sifting unwanted outliers:
Often, some observations do not fit within the data set we are analysing. And sometimes, these outlier observations are the ones that prove the theory of data we are working on. So, remove an outlier only if you have a legitimate reason to remove it.
The bottom line here is that it does not matter if an outlier exists in the data set; outliers are not always incorrect. The step of sifting the data set is needed to validate all the outliers, necessary and irrelevant both. Consider removing the irrelevant ones, and keep the ones that are validating the analysis of the data set.
Handling missing data:
Missing values are not acceptable by most of the algorithms; thus, there is no scope for ignoring any missing data. You can deal with the missing data in two ways mentioned below:
Validate and QA:
After completing the process of data cleaning, you should be able to validate the questions given below:
False conclusions because of unclean data can lead to poor decision making and lousy business strategy. Also, this may lead to a lot of embarrassment while communicating the outcome to the team, realising that the data is not anywhere near the analysis.
Characteristics of a good data:
What are the benefits of Data Cleaning?
Learn Data Cleaning with SRM University and LEARNXT:
Considering the demand for Data Science among the students, SRM University, in collaboration with LEARNXT, is offering various Data Science courses. Students who wish to have their hands-on experiential learning through real-life projects can enrol them in the courses offered by this digital learning platform. The world-class and comprehensive data science curriculum and world-class faculty will help you stand out.
What else?
Being a part of SRM University will fetch you some unmatchable benefits such as:
Stay tuned to LEARNXT to know more about Data Cleaning.
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