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In this article, we have explored the concept of Data Wrangling which is a critical process/ phase in Data Science. We have explored the tools used for Data Wrangling as well.
Table of contents:
- Introduction to Data Wrangling
- Utility of data wrangling
- Importance of the process
- Tools Employed
- Approach to Data Wrangling
| Point | Data Wrangling |
|---|---|
| What is it? | It is the processing of a dataset for further analysis or making it compatible |
| Part of? | A phase in Data Science |
| Tools | Microsoft Excel, Google Sheets, Amazon Web Services, Google Big Query |
Introduction to Data Wrangling
Data wrangling involves processing a data set to various formats of data for the purpose of analyzing or making the data set more compatible with an application a program.
Utility of data wrangling
The amount of data continues to grow exponentially over time, making it critical for the enormous volume of data to be organised in order to result in an effective analysis process.
This is where the process of data wrangling comes in, which involves cleansing and unifying the jumbled data sets.
Importance of the process
It is worth noting that data professionals spend the majority of their time wrangling data while only a small portion of their time actually analysing and modeling data. While the time spent on the process may appear to be doubtful and/or frivolous, it is important to note that it allows an organisation to construct a solid foundation of data sets on which data processing will be performed. Once this foundation is in place, the data handling procedure produces immediate results, which may not be the case if some data wrangling processes are skipped or overlooked.
Such advantages of the data wrangling process make it a necessary aspect of data processing.
Tools Employed
Before processing data, many technologies can be used to organise and clean it. These tools can be automated, or human data munging can be done by a data professional or a team. When the dataset is enormous and the dealing businesses are substantial, automated tools are required. Take a look at some examples of data wrangling/data munging tools:
- Microsoft Excel
- Google Sheets
- Amazon Web Services
- Google Big Query
Approach to Data Wrangling
The process begins with getting to know your data which can look like from anywhere between finding obvious patterns and trends in the data to identifying the issues in it as missing data.
Next, the data is interpreted from its incomplete form to something which can be readily used by the intended applications.
Further, the data is cleaned in such a way that the final result of the processing is affected minimally by inherent errors such as empty cells, non standard inputs,etc.
After these steps, it is decided whether the data necessary for the project is sufficient or not. If not, the data collected so far is then "enriched" using data from other datasets for which the above steps are performed once again.
Next, the data is analyzed to ensure that the data collected so far is both consistent and free of errors. It is possible that various errors may be discovered which are then resolved before proceeding further. This process of validation is usually automated and requires programming knowledge from the data professional.
Lastly, the data is published in either print or electronic form for further analysis by the organization.
With this article at OpenGenus, you must have the complete idea of Data Wrangling.
