Data wrangling may feel like an adventurous ride for the first time, but after some practice, a fresher can get more experience. Data wrangling is the process of finding disorganized or incomplete data and standardized it for easy access, consolidation and analysis.
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Data Wrangling:
Data wrangling is an integral first step of your project. It also includes mapping of data field from source to destination. Usually, newbie uses publically available data set for practice. Before getting your hand dirty, it’s better to understand which tool or skill you want to practice it on.
Before moving on, it’s necessary to know data-wrangling help organize data for further processing or make it compatible with the system or tool. Complex data set can obstruct data analysis and processing. It is necessary to make data set compatible with the target system’s requirements. Earn yourself a promising career in data science by enrolling in the Data Science Training in Hyderabad offered by 360DigiTMG.
Importance of data wrangling:
You can understand the importance and need of data wrangling by knowing that a Data professional almost spent 73% of their work time wrangling the data. Data wrangling helps business owners to make accurate decisions. The process of data wrangling include cleaning and organizing of raw data into a compatible format for use.
Properly wrangled data establishes a solid foundation that quality data is entered for analysis. It also makes sure that quality data use for downstream processes. Data wrangling is essential to enhance the data –to-insight journey and provide timely decision-making.
Data wrangling can be done into repeatable or consistent procedures by using a data integration tool. Some tools use automation features that means software can clean and convert source data into a required format or reuse it as per the target system’s requirements. Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.
Data wrangling in five steps:
Let’s check how to wrangle data in five broad steps.
Step# 1. Understanding Data
The first and foremost step to data wrangling is to focus on “why” in your mind. Understanding the data format and where you will going to use it simplifying next step. Think about the questions that appear in mind while setting the goals. For instance, if you are working on a customer dataset, set goals like
Are you trying to find out at what time of day customers are most like to do online shopping? Are you analyzing a record of active and non-active users of a particular community?
The aim of data analysis is to extract useful information from massive data sets and bring it to the table for decision-making. Stay motivated and keep repeating the goal of analyzing at the center of your project. Also, check these data science colleges in Pune to start a career in Data Science.
Step#2. Structuring data set
Make it possible to have a rich amount of data to extract information. Usually, you will get data in disorganize arrangements and you have to organize it. The second step of data wrangling is to organize or structure the data set for easy accessibility. This process involves a statistical technique which means to condense and extract answers from a stack of data. Try to target at least a few thousand rows and 20-25 columns at a time.
Step#3. Cleansing or removing unnecessary dataset
The third step of data wrangling involves removing or fixing corrupted, incorrect, duplicate, or false data within the dataset. Almost all datasets have some outliers that may affect the result of the analysis. To get optimum results, you will need to change null values, remove special characters and duplicates. You will need to standardize the formatting for further steps.
Step#4 Enriching
The fourth step of data wrangling is to make it enrich or find a diverse range of variables. You will need to make it more authentic by adding supplementary data. Your data should include a mix of categorical and continuous variables.
Step#5. Validating
The fifth step of data wrangling involves validating, which means identifying some repetitive programming steps. The action is used to authenticate the reliability, quality and safety of the data. The validating process helps to find a balance between sparse and excessive datasets. Choosing too many factors gives you poor results, hence focusing on the reliability and quality of data.
Using tools for data wrangling:
You can choose a tool for data wrangling that will help you to save time. This automation tool allows you to extract, transform, clean and structure your data.
Data Wrangling Use-Cases:
Data wrangling is commonly used for diverse use-cases, such as fraud detection and customer behavior analysis.
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