Data wrangling/cleaning is the process of transforming raw data into a format suitable for analysis. Raw data is often incomplete, inconsistent, or contains errors that must be identified and corrected before analysis can occur. Data wrangling/cleaning involves a series of steps to identify and correct these issues, including removing duplicate data, filling in missing values, and identifying and correcting errors.
Key Highlights
- Data wrangling/cleaning is a necessary step in the data analysis process.
- The process involves identifying and correcting issues with raw data to prepare it for analysis.
- Steps in the process include removing duplicate data, filling in missing values, and identifying and correcting errors.
Learn more
- Data Wrangling: Definition, Techniques, and Tools
- Data Cleaning: Definition, Techniques, and Tools
- A Comprehensive Guide to Data Wrangling
Applying the Concept to Business
Data wrangling/cleaning is a crucial step in the data analysis process for businesses. Without clean data, analysis results may be inaccurate and lead to poor decision-making. By ensuring data is clean and consistent, businesses can make better informed decisions and gain insights that can lead to improved performance.
To apply this concept to business, businesses should establish a process for data wrangling/cleaning that includes identifying and correcting issues with raw data, such as filling in missing data and identifying and correcting errors. This process should be standardized across the organization and regularly reviewed to ensure it remains effective and up to date with industry best practices. By doing so, businesses can ensure their data is reliable, accurate, and ready for analysis.
• Data Preprocessing
