This post tries to explain data wrangling by showing you where, when and how it can be used. At the end of the article, I'll share how you can get an in-depth understanding of Data wrangling.
Data wrangling, Data Manipulation, or Data Cleaning is the process of cleaning and unifying messy and complex data sets for easy access. This process of cleaning, organizing, and transforming raw data into the desired format helps the analysts to use data for prompt decision-making.
For Example, identifying gaps in data and either filling or deleting them, and also deleting data that are unnecessary or irrelevant to the project.
What Is The Purpose Of Data Wrangling?
The primary purpose of data wrangling is to get raw data in a coherent shape. It acts as a preparation stage for the data analysis process. It helps us make sense of the flow of the data. As well as helps remove any unnecessary and redundant information which we may not require.
Wrangling also helps us to identify new patterns, behavior and features hidden within the data, which are crucial in the data science process. It is also a way by which large amounts of data can be labeled and organized for easier identification and further processing or analysis.
Data wrangling is an essential step in the data science process and can be considered the first step toward understanding the data.
What can we achieve with data wrangling?
Cleaning data helps improve usability as it converts data into a compatible format for the end system. Data wrangling can also help build data flows that ensure efficient scheduling and automation of data through the database systems. This, in turn, helps users to process massive volumes of data easily and easily share data-flow techniques and much more. Data Wrangling can be used for the following:-
Data security -
Data wrangling helps to arrange and structure data. It can also help us identify key features of the data.
This will help us clean out the data and remove any unnecessary entries or observations. Which keeps the data better organized and secure. It also helps make sure there are no mistakes in newly added data and ensures all input is validated.
Fraud Detection -
When there is a huge amount of continuously growing data, it is difficult for the human brain to process changes in patterns and features of certain data.
But data wrangling can help us validate huge amounts of data in very less time. For example, we can detect fraudulent credit card / Bank transactions by setting up automated data screens to check the patterns. Outliers in these transactions can be further investigated to detect fraudulent transactions.
Data Reliability -
Data cleansing ensures you only have the most recent files and important documents, so when you need to, you can find them with ease.
Wrangling helps to ensure the data is correct and up to date. It helps businesses reduce duplication of data and helps remove unnecessary redundancy in-store data. This in turn helps reduce costs.
It makes sure that we have access to the most updated and accurate data as quickly as possible.
Where to learn?
The Process of Data Wrangling is not straightforward, and there is much to learn on this topic alone. Learning the structures and types of data is essential. You should also gain a foundational understanding of the Data Science Process.
You must also be familiar with the tools that are used for data processing. Learning how to use spreadsheet tools is a must. I’d recommend starting to learn with Microsoft Excel. After you master a spreadsheet tool, you can then move on to learn Python or R. You can use this link to learn about the other Tools used for data wrangling.
If you like to read, here are a few books that, I think, are wonderful and really insightful—
- MICROSOFT EXCEL 2019: DATA ANALYSIS&BUSINESS MODEL.
- Principles of Data Wrangling: Practical Techniques for Data Preparation.
- The Data Wrangling Workshop: Create your own actionable insights using data from multiple raw sources.
- Data Wrangling with Python: Creating actionable data from raw sources.
- Effective Data Wrangling and Exploration with R.
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