Data wrangling is a process of cleaning and managing unstructured or messy data to make it easily accessible and prepared for analysis. As a company collects enormous amounts of data that needs to be cleaned, doing it manually, without using any tool, is difficult. At the same time, machine learning can make this process easier. One can build the machine learning skills to make a successful career in the same domain by joining a machine learning course online.
In this post, you will understand data wrangling and how machine learning helps to make the data wrangling process easier.
What Is Unstructured Data?
Before knowing about data wrangling, first, it is essential to understand what unstructured data is. Unstructured data is any data that does not adhere to a data model and has no apparent organization, making it difficult for computer programs to use. Unstructured data is not well suited for a standard relational database since it is not organized in a predefined way or does not have a predefined data model. The unstructured data is also known as big data.
Impact of Big Data
Big Data has made a significant impact on various industries. It has made it feasible for researchers, analysts, and data scientists to observe trends, forecast values, and study their environment. This is a result of the enormous amount of data we now have. We can, for instance, use a person’s demographic data, transaction data from numerous platforms, and data gleaned from publicly available reviews, comments, etc. This can help us fully comprehend that person. And we can do this for millions of records, enabling the companies or businesses to develop niche products.
As a result, a user or an organization must overcome a range of challenges to harness the value of Big Data.
Data Wrangling – Introduction
Big Data and data wrangling are inter-related terms. Big data can help businesses gain novel insights. Still, the difficulty is that we can’t use the data as it currently exists because it comes from so many different sources and takes so many different forms. Here is where data-wrangling comes into place. Data wrangling is taking data that is frequently unstructured, raw, chaotic, messy, complicated, incomplete, etc., and making it appropriate.
The standard analytical and modeling methods can use this appropriate or wrangled data for further consumption. Following the completion of data wrangling, various processes, such as data mining (which includes exploratory data analysis, visualization, and descriptive statistics), bivariate statistical analysis, statistical or machine learning modeling, etc., might start.
Steps Include in Data Wrangling
Data wrangling requires users to perform manual steps before the data can be utilized. Nevertheless, most people agree that there must be a sequence of stages to complete this process. The typical Data Wrangling steps are mentioned below:-
- Identifying the data
- Understanding the identified data
- Data Structuring
- Cleaning the data
- Data enriching
- Validating the data
- Publishing the validated data
Importance of Data Wrangling
It is frequently impossible to use big data without cleaning it for analytical purposes, as the data is vast and unstructured. Thus, the above-mentioned data-wrangling stage steps are required to extract meaningful insights from the data. Data wrangling significantly alters the data in such a form so that the data analysts can further take action using it. The following terms state the importance of data wrangling:-
- It structures the data so that it may be easily changed, mined, analyzed, etc.
- It understands data noise, which can negatively impact analysis.
- It highlights the hidden or masked information, enhancing the understanding received from the dataset.
After going through all the data wrangling steps, the user also gains a better understanding of the type of data they are working with, which can be helpful when performing analytical and predictive tasks.
Role of Machine Learning in Data Wrangling
The fundamental tenet of machine learning has always been ‘the machine will write the code for you rather than the user creating the code.’ This has changed how we operate in various ways, increasing speed, accuracy, efficiency, and the number of resources needed to complete a task. Data wrangling is a time-consuming stage preceding data analytics and predictive modeling, which are now parts of the data science process. Machine learning can be applied in this situation to feasible the task of data analysts. Some automated data wrangling tools can help ease the data wrangling process, such as R, Python, and Excel. But, these tools are insufficient to work with big and complex data.
Machine learning methods can be utilized to aggregate different data sources and create them in a structured form. Creating machine learning models operating in a supervised learning setup is critical to comprehending the data. Developing classification models that can scan the data for specified patterns is also possible. The data wrangling procedures can then be automated using machine learning techniques.
Conclusion
Hence, machine learning plays a significant role in easing data wrangling. Data wrangling is essential for every organization, and machine learning can ultimately save time by automating the data wrangling process. Every organization needs a machine learning professional who can create tools to shorten the data wrangling process. If you are also willing to make your career in machine learning, you can join a certification course and dive into the same domain after equipping the relevant skills and knowledge. Hero Vired is one such institute offering machine learning classes to interested individuals. You can visit their website to get complete information about the course.
