What is Data Wrangling?
Data Wrangling, also known as data munging or data cleaning, is the process of transforming and mapping raw data into a structured and more usable format for analysis, reporting, or machine learning purposes. Data Wrangling involves various techniques, including data cleaning, data transformation, and data enrichment, to prepare the data for subsequent steps in the data analysis pipeline.
What can Data Wrangling do?
Data Wrangling addresses common data quality issues and prepares data for further analysis:
Cleans data: Data Wrangling involves identifying and correcting inconsistencies, errors, and inaccuracies in the data.
Structures data: Data Wrangling organizes data into a structured format that can be easily analyzed, such as tables or relational databases.
Integrates data: Data Wrangling combines data from multiple sources and formats, ensuring consistency and compatibility.
Transforms data: Data Wrangling applies transformations, such as normalization, encoding, or aggregation, to make data suitable for specific analytical tasks.
Some benefits of using Data Wrangling
Data Wrangling offers several benefits for data analysis and machine learning:
Improved data quality: Data Wrangling helps ensure data quality by identifying and correcting inconsistencies, errors, and inaccuracies.
Efficient data analysis: Data Wrangling streamlines data analysis by organizing data into a structured and usable format.
Better decision-making: Data Wrangling enables more accurate and informed decision-making by providing clean and reliable data for analysis.
Increased productivity: Data Wrangling automates time-consuming and repetitive data cleaning tasks, allowing data professionals to focus on more critical aspects of their work.
More resources to learn more about Data Wrangling
To learn more about Data Wrangling and its applications, you can explore the following resources: