What is Data Wrangling in Data Science?

What is Data Wrangling in Data Science? Do you have a predictive analytics mindset, are curious to find out more, think outside the box, and have storytelling sensibilities? If yes, then the field of data science might just be for you.

You can explore the job role of a data scientist who is concerned basically with the data, the hidden trends and correlations that can be extracted from it, and the stories they can show.

Such professionals have a clear understanding of programming languages like Python, R, SQL, data visualization, machine learning, statistics, Apache Spark, and many other related technologies. 

Generally, if your role comes under data science, then you should definitely be aware of the data science lifecycle. Here are the processes involved in the data science lifecycle – business understanding, data understanding, data preparation, exploratory data analysis, data modeling, model evaluation, and model deployment.

This article basically deals with the data preparation stage of the data science lifecycle or, more precisely, data wrangling. When we say data preparation, it means selecting appropriate data, integrating it, cleaning it, and testing for outliers. Data wrangling is nothing but the data cleaning part of the overall data preparation. 

Let us know more about data wrangling and why it is an important part of the data science lifecycle.

What is Data Wrangling in Data Science? Here’s the Explaination

When you decide to pursue any Data science certification, you will surely come across the process of data wrangling.

The term is used to describe the steps taken to remove errors and combine complex data in order to make them more accessible and easier for analysis.

We know that the raw data collected from various sources is of no use until it is analyzed effectively. For analysis, the data needs to be in a usable format, which is what is performed in the data wrangling process.

When you have a data set with missing values, duplicate values, or corrupt values, the analysis may go wrong, and the insights may show you a completely different picture that can be misleading for making business decisions. 

Data wrangling may involve merging multiple data sources into a single dataset, identifying missing values and either removing them or filling them, deleting duplicate values, getting rid of corrupt information, identifying extreme outliers in data, and taking appropriate action.

Data wrangling is performed by data management, business intelligence, or information technology teams when they want to integrate data sets into a warehouse, data lake repository, or another storage system.

More often, a data engineer is responsible for this work; however, in certain organizations, it may be performed by data analysts or data scientists. Nowadays, a number of self-service data preparation tools are available in the market that can help data professionals clean data effectively.  

Steps Involved in Data Wrangling 

Different organizations follow different steps when it comes to preparing data as part of the data science lifecycle. However, these are the general steps taken into account by most companies. 

  • Raw data is first collected from disparate sources like electronic equipment, social media, data warehouse, surveys, experiments, information logs, and so on. 

  • Data professionals then dive into the collected data to understand what they represent and how they can be prepared for the intended use. It is at this step that inconsistencies, missing, or corrupt values are identified. 

  • Next, data cleansing takes place, meaning correcting the issues identified in the above step. Duplicate and corrupt values are removed while missing information is either filled or eliminated.

  • After the data is cleaned, it has to be organized and then converted into a usable format. The data may exist in different formats, but for analysis, one format is beneficial and can be given as input for any data analysis tool. 

  • The prepared data is finally checked for completeness, consistency, and accuracy. If it meets all the factors satisfactorily, then it is ready for the next phase of the data science lifecycle – data analysis. 

Why is Data Wrangling an Important Part of the Data Science Lifecycle?

More often, you will hear that data mining and data analysis are the processes that generate actual business value.

However, for data to be effectively analyzed, it is important that the data is cleaned and prepared.

If data wrangling is not conducted, then the results generated by data analysis may divert from the expected outcomes or can mislead to wrong conclusions.

Data scientists cannot begin with the analysis of data, and most of their time is spent in collecting, cleansing, and structuring data if data wrangling is not performed. 

The process of data wrangling helps in making sure that data analysis will lead to reliable outcomes. It helps in identifying data issues at an early stage so that they do not affect the analysis part of the life cycle.

A company can further reduce data management and analytics costs by incorporating data cleaning.

The prepared data can also be used as input for other applications, and companies can generate higher Return on Investment (ROI) from their business intelligence initiatives. 

Now, if data wrangling has grasped your attention and you are ready to learn more about it, then enroll in a data science online course and dive into the field. 

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