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Produced by: Analytics Drift
Data wrangling is a collection of processes, including cleaning, organizing, structuring, and enriching raw data to transform it into a readily usable format.
Data wrangling is necessary for the data science process as it delivers information through analysis and enables the transformation of data into the desired format.
1. Making raw data usable 2. Easy data collection 3. Detection of noise or flaws and missing observations 4. Business-oriented approach 5. Quick decision-making 6. Visualization of data
Each step in data wrangling offers to manipulate data to understand better and extract information hidden in the data
The first step of data wrangling is to discover. As simple as it sounds, discovering data means getting to know the data and conceptualizing how you can use it.
As the collection of data may come from more than one source, the data required to be restructured and organized to make it more manageable for the analytical model.
Data cleaning consists of tasks dealing with errors, including duplicate entries, invalid values, and null values using programming languages like Python, R, and SQL.
This step determines if the data needs to consider external data for better performance and to fill the gaps in the data (if any) to derive meaningful information.
This step checks the quality of the wrangled data and verifies whether or not the data has quality, consistency, accuracy, security, and authenticity.
Publishing is the final step in data wrangling, where the wrangled data output is ready for analytics.
Companies prefer skills like data annotations, web scraping, and data transformation, including merging, ordering, aggregation, and so on.
The tools used for data wrangling include programming languages, software, and open-source data analytics platform. Some tools are MS Excel Power Query, Python and R, Alteryx APA, and more.