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Are you a person who uses spreadsheets? Would you obsess over keeping your data organised? Do you think a workable format of data helps to interpret it effectively? If yes, then with or without your knowledge you are wrangling the data. Surprised? Now, let’s discuss in detail, “What is Data Wrangling”, an inevitable process in this internet world.
What is Data Wrangling?
It is the process of converting raw into clean data for useful insights and streamlined analysis. Of course, Data Wrangling is one of the essential fields in data science. It is usually done after data pre-processing.
The term “data wrangling,” coined in the contemporary context of “agile analytics,” aims to describe the majority of time spent dealing with data. Furthermore, the conversion of raw data into a compatible format is carried out for many purposes. It includes real-time research, data accuracy, data analysis speed-up and so on.
Data Wrangling also known as Data cleaning, Data Remediation, and Data Munching boosts data usability. It can be done either manually or automatically, however, methods vary depending on the data leveraging. The most familiar data structure used to wrangle the data is the data frame; after all, it’s intuitive and possibly protean.
To advance one’s career in the data science field, it is crucial to study “What is Data Wrangling” and the importance of Data Wrangling.
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Importance of Data Wrangling
- Every piece of information is significant in this age of big data. However, organising voluminous data is equally important as gathering the data. Clean and organised data results in beneficial output. On the contrary, employing poor raw data will lead to scepticism about the outcome.
- The cost of preventing raw bad data is lower compared to the cost of fixing the problematic data. When it comes to AI and machine learning, for instance, if you create a model using bad data, the resulting model will perform poorly.
- Data wrangling turns out to be the most crucial step in the analysis. According to data scientists, better data is even more crucial than having the most effective algorithms.
- Downstream processes are meaningless if it is not started with excellent data.
- The foundation of the building should be robust although it is time-consuming. However, it is extremely necessary to keep the building strong and durable for decades. Similarly, Wrangling data is important in analysing to acquire insight and expedite data processing although it is time-consuming.
- Makes raw data into quality data.
- Organisations can achieve timely decisions with more accurate data.
- Assembling all data from numerous sources into a single location for data analysis.
- To spot significant outliers.
- Deletes and cleans the irrelevant and unnecessary data, which enriches the data understanding.
- Undoubtedly, understanding “What is Data Wrangling” not only helps with time-saving but also with cost-effectiveness while handling the data.
Benefits of Data Wrangling
In order to manage and prepare data for analysis, data scientists spend eighty per cent of their time. But, it is worth it as the benefits are endless.
- Data Wrangling reduces unnecessary complications. It simplifies complex data into a versatile and compatible format for more accurate analysis.
- It helps users simply process very large volumes of data and exchange data-flow strategies.
- Distinguishes different sorts of data depending on the generated information.
- Enriches data for behavioural research and company insights.
Data Wrangling Process
Eventually, the process of knowing data wrangling is mandatory in the study of “what is Data Wrangling”.It consists of the following 6 steps.
First, you should know what kind of data you are going to use. Second, you should get an overview and familiarise yourself with the data. Third, get clear with the points to be removed.
The process of preparing your data so that it is consistent and prepared for analysis. The type of analyses and what raw form your data is in will determine how you organise your data.
Third, the process of eliminating errors that could harm your analysis in the future. For instance, empty spaces, blank cells, incorrect words and so on.
After you clean your data, you must decide whether you have all the data needed for analysis. Occasionally, the cleaned data might be less for the downstream process. This is where you have to enrich your data from external and internal sources such as social media reviews and revenues respectively.
It ensures that your enriched data is reliable, consistent, and well-structured.
You can now share your validated data. Finally, you need to get your data ready before publishing. In other words, you are distributing the data inside your company or organisation to meet various analytical requirements. Additionally, you are exporting data to machine learning applications to run through previously learned models or to train new ones.
Data Wrangling Tools
Some of the Data Wrangling tools facilitate data processing. On the other hand, others can help make data more organised and understandable. However, each one is beneficial to professionals as they manage data to the advantage of their organisations.
Organisations that deal with extremely large data volumes must automate data cleaning. The data team or data scientist is responsible for handling manual data cleansing activities.
When learning about “what is data wrangling,” it is necessary to be familiar with data wrangling tools. The following is the brief list.
A spreadsheet platform is used to store and catalogue data.
A straightforward, user-friendly tool used for all datasets.
A data cleaning tool that has to be programmed.
An open-source tool that cleans and transforms messy data into another format which needs programming skills.
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An interactive tool for data cleansing and transformation. Moreover, Spend more time analysing your data and less time preparing it.
A tool to convert Excel data format to internet-friendly format. For example, JSON.
For data preparation and cleaning.
A tool that supports an enormous amount of data. More than 100 already designed Data Wrangling Tools are available in Alteryx. Not only it covers topics including data profiling but also deals with find-and-replace and fuzzy matching.
Indeed, the following real-time examples help you better comprehend “What is Data Wrangling”.
Examples of Data Wrangling
- Organising account information, client payments, and staff benefit data for a large firm. Besides, it may generate hundreds of millions of dollars in revenue each year.
- In healthcare units, bills, medicine dosage, donations received, and patient lists are the pieces of information which should be kept organised. Undoubtedly, data wrangling is crucial in the end.
- Combining information from different databases and sources into a single data set.
- Filtering data based on regions, demographics, periods, etc.
- Removing blank spaces between text in a document or blank cells in a spreadsheet.
Significant Data wrangling skills
- Being a data scientist you should have expertise in Data wrangling. You should possess the skill of cleaning raw data, deleting outliers, removing null values, and converting the data into a usable format in other programmes.
- As a data scientist, you should know how to use data from different sources.
- You may analyse datasets, spot trends, generate visualisations, forecast future data, and more using open-source programming languages. For instance, R, Python, etc.,
- Besides programming, database management helps data scientists to archive, read, and upgrade data.
- Machine learning is the Artificial intelligence that enables data scientists to work with very massive data sets.
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Data Wrangling ensures that you employ the most accurate and cleanest data. Also, it positions you for a successful workflow later on. It can be a laborious procedure. Even though, the effective outcomes will make you glad as a Data Analyst that you included it in the data analytics tools.
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Frequently Asked Questions
Data Wrangling is the procedure or process of converting raw data into a clean, structured, error-free format. Not only Data Munging but it is also called Data Remediation.
ETL (extract, transform, load) is a method for integrating data whereas Data Wrangling is the process of extracting data and turning it into a usable format.
Comparing ETL, Data wrangling is a less structured process.
Of course. In order to get the data organised, clean, and suitable for use in machine learning procedures, report production, and associated procedures, data analytics teams frequently spend 50–80% of their time working on these tedious chores.
In India, the average salary of a Data scientist ranges from 3.6 Lakhs to 26.0 Lakhs Rupees according to AmbitionBox estimate.
Undoubtedly, Yes. A job in data science offers great opportunities for future growth. Moreover, LinkedIn and Glassdoor labelled Data Scientists as “the most promising career” and the “best job in America” respectively due to its high demand, attractive salary, and plenty of benefits.
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