How Can You Effectively Clean And Prepare Data For Analysis? Complete Tutorials

It is important to know how can you effectively clean and prepare data for analysis. Extracting insights from data is as good as the data you are using for analysis and extraction purposes. It is important to convert data into a structured format before using it for analysis to gain crucial insights.

Data Cleaning is also known as Data Cleansing which is one of the most important phases in data analysis. In this blog, we will learn how to clean and prepare for data analysis.

Table of Contents

What is Data Cleaning?

Data Cleaning is the initial phase of data analysis which is the process of fixing or removing corrupted data within a dataset. When you are extracting data from various sources then you will have to look for methods of data cleaning. There is not an exact predefined method of cleaning data but we can note down some crucial methods in data cleaning.

Why Is Data Cleaning Important In Data Analysis?

how can you effectively clean and prepare data for analysis

Data Cleaning is an important stage in data analysis which makes sure that the extracted insights are free from any kind of irrelevancy, inconsistencies, etc which can alter the process of analysis. Cleaning the data makes it suitable for effective analysis and creates a foundation for analysis.

Cleaning data makes it organised to keep it tidy which helps it in storing data more effectively and securely.
If the database is in good order it will provide you with correct information which without cleaning might provide misleading information.
It improves the productivity of the data being analysed and makes production easier.
It avoids unnecessary cost on unnecessary data and gives you a chance to correct the errors before they use excessive and consume time and resources.

Best Practices for Effective Data Cleaning

how can you effectively clean and prepare data for analysis

You need to follow best practices for data cleaning to ensure effective data analysis of the available dataset and learn How can you effectively clean and prepare data for analysis.

It is important to understand the objective of data cleaning and your ultimate goals before starting with the process.
You can adopt automation tools and plugins to integrate automation in data cleaning tasks.
Make sure you follow the proper guidelines and workflow for data cleaning. It is important to maintain proper documentation of your data.
It is important for you to maintain a detailed report and overview of every step on the way. You can refer to these documentations in future for help.
Make sure you ensure accuracy to ensure quality for the dataset. Make sure you ensure the validation is being carried out in the process.
Keep backup and recovery of every data and make sure it is updated in your system.

How Can You Effectively Clean and Prepare Data For Analysis?

how can you effectively clean and prepare data for analysis

Let us walk through the process of cleaning and preparing data for analysis purposes. The more structured the data is the easier it is to extract insights from it.

1. Clear Redundant Or Duplicate Data

Start with looking for duplicate or irrelevant data in your dataset. During data collection it might happen that you extract duplicate and unnecessary data elements. When you combine data from various places it might happen that you end up creating multiple duplicate data.

Using Python Programming, you can handle duplicate data easily. Check for duplicate data by using the following syntax.

df.duplicated().sum()

To remove duplicates you only have to set “inplace=True” which can help you eliminate the duplicate items from the dataset.

2. Handling Missing Data

You will have to handle missing value using the distort analysis in the method. When you handle missing data you will be able to make the data structure more efficient and minimize distraction from your memory and dataset.

You can drop observations having missing values inside but make sure you do not drop any important information along with it as it might backfire you ahead. You can input a simple constant value based on other observations in the missing data.

3. Fix Structural Errors in Data

You can fix structural errors in the data so that the analysis goes effectively. You must take care of naming conventions, typos errors, incorrect capitalization, which can fix the structural errors easily.

The outliers need to be fixed in the data which can be detected using boxplots, Z-score methods, IQR method in data analysis.

4. Handling Inconsistent Data

There can be places where your data in the dataset is not arranged properly and you need to remove or fix those inconsistencies which might be due to human errors, formatting errors, typos mistakes, and more.

There might be cases where you will face inconsistencies in units, date formats, or inconsistent naming of entities.

5. Feature Scaling

The feature scaling is a method which ensures that the numerical features have equal importance in machine learning models. It is important to conduct standardisation and normalization in your dataset to ensure proper scaling.

6. Debugging for Errors

Looking for errors and fixing errors is an important step in data analysis which demands proper methods for anticipating errors and fixing them first hand. It might be due to technical glitches, external factors and other issues that might damage the data. You can use advanced tools like Trifacta Wrangler, DataPrep to automate the debugging tasks and fixing for errors.

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How can You Effectively Clean and Prepare Data For Analysis FAQs

Q1. What is data cleaning?

Ans: Data Cleaning is the initial phase of data analysis which is the process of fixing or removing corrupted data within a dataset. When you are extracting data from various sources then you will have to look for methods of data cleaning.

Q2. Why is it important to clean data before analysis?

Ans: Data cleaning is an important process before analysis as it prepares data for analysis and reduces the unnecessary and irrelevant information from the dataset.