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Tutorial: Data wrangling with Tableau and Excel Caitlin Harley Last edit: September 16, 2016, Introduction: Data wrangling is the process of preparing raw data for use in a data analysis or visualization
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01
Start by gathering all the necessary data that needs to be wrangled for the tutorial.
02
Organize the data in a structured format so that it can be easily manipulated.
03
Clean the data by removing any irrelevant or duplicate entries.
04
Handle missing values by either imputing them with appropriate values or removing them from the dataset.
05
Transform the data by applying various techniques such as data normalization, standardization, or feature engineering.
06
Explore the data using descriptive statistics and visualizations to gain insights.
07
Apply data wrangling techniques such as filtering, sorting, merging, or reshaping to prepare the data for analysis.
08
Validate the wrangled data to ensure its quality and correctness.
09
Document the data wrangling process and provide explanations for any decisions made.
10
Test the wrangled data with different analysis or modeling techniques to ensure its suitability for the intended purpose of the tutorial.

Who needs tutorial data wrangling with?

01
Data analysts who want to learn how to clean and prepare data for analysis.
02
Data scientists who want to enhance their data wrangling skills to improve the accuracy of their models.
03
Researchers who rely on data for their studies and need to ensure the data is in a suitable format for analysis.
04
Students or professionals who are new to data wrangling and want to learn the fundamentals.
05
Business professionals who want to gain insights from their company's data but need to first clean and manipulate it.
06
Data engineers who work with large datasets and need to optimize the data wrangling process for efficiency.
07
Anyone who deals with messy data on a regular basis and wants to make their data wrangling tasks more systematic and effective.
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Tutorial data wrangling is typically done with tools such as Python, R, or SQL.
Anyone handling data and needing to clean, structure, and prepare it for analysis should file tutorial data wrangling.
Tutorial data wrangling can be filled out by following the step-by-step instructions provided in the tutorial or by using data wrangling software.
The purpose of tutorial data wrangling is to ensure that the data is clean, structured, and ready for analysis to derive meaningful insights.
Information such as data source, data cleaning techniques used, data transformation steps, and data validation processes must be reported on tutorial data wrangling.
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