What is Data Cleaning in Data Science

Data Cleaning in Data Science is correcting or eliminating inaccurate, corrupted, poorly formatted, duplicate, or incomplete data from a dataset. 

For instance, when there is a big pile of data, some can be useful, and some are not, but it's all mixed. Therefore, to make it suitable, there is a need to go through the pile of data and separate the good data from the bad data. This is precisely what data cleaning is about. 

Need for Data Cleaning

• Data Cleaning in Data Science is one of the initial steps of the workflow. It is a crucial step. This is because it helps enhance the quality of the data and make better business decisions.
• Data collection is tedious. Companies receive a lot of data regarding their clients, products, employees, etc. These data can be addresses, bank details, contact numbers, etc. Regular cleaning of this data can make the database look tidier and more organised. This helps remove inconsistencies.
• Apart from data scientists, other company departments rely on data too. For instance, marketing departments refer to their customer's database to send personalised emails to their customers regarding new offers or deals. Having incorrect data can lead to sending emails to the wrong customer. This creates miscommunication. Hence, cleaner data leads to satisfied clients.
• Organised and clean data will save time for everyone in the team. This is because they can quickly extract the required data from the database since irrelevant data is already eliminated. As a result, the team becomes more efficient and productive.
• The higher the quality of the data, the better the decision-making process. Bad data can cost many unwanted consequences for the company. This is because its decisions will be biased. IBM states that bad data costs the U.S. over $3 trillion approximately every year. Therefore, regular data cleaning can help a company take the right path and make profits.
• Data scientists have to build machine-learning models. These rely on data to predict specific outcomes for the company. A good model will yield results with higher accuracy. This will help in predicting certain aspects of a company.

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Ways of Data Cleaning in Data Science

There are many ways to clean data. The specific methods used can depend on the data's nature and the project's needs. Here are some standard techniques for cleaning data, What is the use of Machine Learning for Data Science

Handling missing values

Data collected manually often has missing values. This leads to inaccurate results if fed into a model. One solution is to replace missing values with a placeholder, such as "NULL" or "0". 

Dropping rows or columns with missing values is not recommended. This is because it can result in the loss of information. A better approach is to use techniques like mean or median imputation to fill in missing values. 

Also Read: Data Engineer vs Data Scientist

Fixing structural errors

Anomalies in structure occur during data measurements or transfer. These are encountering inconsistent naming conventions, typographical errors, or improper capitalisation. These discrepancies can lead to the misclassification of categories or groups. For instance, when "N/A" and "Not Applicable" are present, they should be considered a single category.

Also Read: Is Data Science a Good Career?

Removing irrelevant data

Identifying and eliminating irrelevant or unnecessary data is vital before beginning the data-cleaning process. Removing insignificant information such as email addresses when analysing a customer's age range can help simplify. It can also speed up the analysis process. Additionally, excessive blank spaces between text can be removed for better readability and cleaner data.

Changing data types

A data frame is a collection of diversified data. This includes categorical, object, numeric, and boolean. So, when cleaning the data, one of the most common tasks is converting numbers to their appropriate format.

Numbers entered as text must be converted to numerals to perform mathematical equations. Similarly, dates stored as text must be converted to a numerical format for operation or analysis. It is essential to convert data types to process and handle data appropriately.

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Removing duplicates

Collecting data from various sources or scraping it may result in duplicate entries. These duplicates may be a result of human error. Having duplicates in your data can negatively impact your analysis. It can make the data difficult to interpret. So, it is advisable to eliminate them as soon as they are identified.For more updates about Data Science Read This: Supervised and Unsupervised Learning

Handling outliers

Outliers are observations that do not seem to align with the overall pattern of your data. They can significantly impact statistical analyses and modelling. Hence, they may need to be removed or transformed. So, it is essential to consider removing outliers if they were caused by improper data entry or didn't align with the analysis. 

Normalisation

Normalisation involves transforming variables. This is to make them have comparable scales. This can be useful for comparing variables that are measured on different scales (e.g., dollars and kilograms) or for reducing the impact of variables with large scales on statistical analyses.

Data concatenation

As data science continues to evolve, the amount of data generated is growing daily. It may be stored in multiple separate files to manage this large volume of data. So, while working with various files, one can use the process of concatenation to combine them for ease of use.

Concatenation, specifically in the context of databases, refers to combining two or more separate entities into a single, larger one. This more extensive database can be a single reference source for all the tasks, Also Read: Numpy vs Pandas

Filtering

Filtering involves selecting only a subset of the data that meets specific criteria. For example, one might filter a dataset to include only data from a particular time or data within specific numerical ranges.

Aggregation

Aggregation involves summarising data by grouping it and computing a summary statistic (e.g., mean, sum, count). This can be useful for reducing a dataset's size or creating a new summary variable.

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Final words

Data Cleaning in Data Science ensures that the data used for analysis is accurate, reliable, and consistent. That is why leading companies prefer to hire a professional with Data Science Certification. After all, clean data ensures optimum outcomes. With expert trainers having 20+ years of experience, 6 Months of Certified Project Experience, and a 100% Guarantee, enrol in this Data Science Online Course and take your career to a new height.