Data Wrangling is a vital part of the data science workflow. It is the process of applying the right tools and techniques to transform raw data into useful information. Data Wrangling has different forms, but regardless of whether it's cleaning, mapping, or transforming, it all boils down to one concept — making things as readable as possible so you can get the most important insights hidden within your data. In other words, it is "getting data into shape."

When dealing with large data sets, you need a way to organize and manipulate the data. Right?!

In this blog, we will discover all you need to know about data wrangling and its use in data science projects. 

The first thing you need to know about data wrangling is that the term itself confers both a sense of ambiguity and importance. It is ambiguous partly because it is an umbrella term for many different tasks associated with data analysis. It is important because these tasks are crucial to how data science work gets done behind the scenes.

What is Data Wrangling? 

Data wrangling is the process of cleaning, organizing, merging, and transforming data sets. It is a crucial part of data science because it provides an opportunity to organize and clean data correctly, which in turn, prevents errors from creeping in later on. Data wrangling can be performed manually or by using a tool. Their language, SAS, SQL, and Excel are the most widely used tools.

The goal of data wrangling is to ensure that your dataset has been cleaned up so that it's ready to be used by your analysis tool. This includes:

• Working with missing values 

• Dealing with missing categorical variables

• Transforming your data into something that other tools can understand

Data wrangling is important because it allows you to use your analysis tool more effectively. If you have too many issues with missing values or other errors in your dataset, then it will be difficult to get any meaningful results out of your analysis tool. With data science training in Chennai, you can master these for better analysis. 

Importance of Data Wrangling

Did you know that data science professionals only use 20% of their time for exploration and modeling while they spend roughly 80% of their time wrangling the data?

So you might think, is wrangling data worth the effort? 

Well, given all the perks data wrangling offers, it's undoubtedly worth your time.

Here are some of the perks of data wrangling: 

Simple data handling

The Data Wrangling method turns unusable and unstructured data into usable data organized into rows and columns. Additionally, the method enhances the data to give it deeper intelligence and greater relevance.

 Makes analysis Easier

After Cleaning, Business analysts and stakeholders can analyze even the most complicated data quickly, easily, and effectively once raw data has been tamed and converted.

Efficient use of Time

 By using the Data Wrangling approach, analysts can spend less time attempting to arrange disorganized data and more time gaining insights to support them in making decisions based on simple and understandable data.

Clearer Visualization of Data

Once you've sorted out the data, you can quickly export it to any visual analytics platform and start summarizing, sorting, and analyzing the information.

Data Wrangling Tools

Many different types of data-wrangling tools exist; each has its own purpose and uses. Some tools help with data processing, while others attempt to organize data and make it simpler to read and understand. Others provide comprehensive Data Wrangling solutions. You must select the best tool for Wrangle Data to benefit your business. 

Some of the major data-wrangling tools are: 

1. Excel Spreadsheet – It is a basic data mining tool for analysts. The spreadsheet is a grid to organize and manage tabular data in rows (or columns). You can use them to organize data in rows and columns, which is useful for organizing information about your customers or products in categories such as age groups, gender breakdowns, etc. 

2. Python and R – Data cleaning with the help of programming languages

3. Tabula – Compatible with all data types

4. OpenRefine – An automated data cleaning tool with the help of programming skill

Steps involved in Data wrangling 

Discovering

Discovery is the initial stage of the Data Wrangling process. This is a general phrase for understanding or becoming acquainted with your data. To make your data easier to use and analyze, you must examine it and consider how you would like it to be arranged.

It is necessary to perform wrangling, which splits the data following specific criteria. Understanding how to locate the Holy Grail is essential in the realm of data.

Structuring

There are many different sizes and formats of raw data as it is collected. It lacks a clear framework, which indicates that there is no established model for it and it is utterly unorganized. It needs to be reorganized to conform to the analytical model used by your company, and providing its structure enables more accurate analysis.

Most of the data is unstructured, and that's where data is parsed. Parsing is a method for obtaining relevant information from recent data.

Cleaning

Raw data typically contains a number of errors that must be corrected before moving on to the next step. Data cleaning includes addressing outliers, making corrections, completely removing bad data, etc. This is accomplished by cleaning and tidying up the dataset using algorithms. Several tools, including Python and R, can automate various algorithmic tasks, which can be learned in data science certification courses in Chennai,

Enriching

Enriching adds extra meaning to the data from the data that has already been cleaned and formatted. In this step, the data is derived into new types. This is where data can be categorized into several types. 

However, it is important to note that enrichment is optional and performed only when the outcome doesn't meet the requirements. 

Validating

Data quality rules are utilized to analyze a particular data set's quality. The quality and accuracy of the data are confirmed after processing, creating a solid barrier to security concerns.

Data validation rules call for recurrent programming procedures that help to verify things, including quality, consistency, and accuracy.

Note: Given the possibility of errors, this step might need to be done numerous times.

Publishing

The last step in data wrangling is publishing, which reveals the primary goal of the entire wrangling process. The objective of the data is to be used later. Therefore data analysts arrange the jumbled data for that. The resulting data must be formatted correctly for the intended audience. The processed data can now be applied to analytics.

Now that the data has been cleaned and analyzed, it's time to share your findings with your teammates. Since you managed the entire process, you're an integral part of this analysis cycle, so you should be there when they wrap things up.

 Remember—you want to make sure that they get everything they need out of the data.

Conclusion:

To sum up, data wrangling has important tasks to execute from the first step of data preparation until the last step of data visualization. The rest of the loading process can be broken down into managing, filtering, and storing datasets. For the most part, data wrangling should be considered a preprocess to any statistical analysis. It is analysts' opportunity to take the results generated by their algorithm and turn that data into something they will be able to interpret. More so than in other fields, a data scientist truly has to work with the data available and not simply on data collected for the sake of collecting it.

Would you like to develop your data science and analytical skills? Learn more about the data science course in Chennai and how to manage data to generate insights and address business decisions. Discover more about what it means to be a data analyst.

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