Module 3 Presentation

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Southern New Hampshire University *

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Computer Science

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Jan 9, 2024

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pptx

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Data Cleaning Methods Power BI vs. Rattle/R for Data Cleansing
Why We Need Clean Data: In its current form, the bank customer data set is yielding questionable results after analysis. Without clean data, any further attempts of analysis will be unreliable. Issues In Data Set: The current data set has many issues which require data cleaning methods including: - Missing/Blank Data - Misspelled Data/Inconsistent Spellings - Special Characters/Inconsistent Formatting - Statistical Outliers/ Duplicate Data - NULL and N/A Values Importance of Data Cleaning: Clean Data is crucial to the accurate outcome of data analysis, and decision- making. Unclean data can waste time, resources and productivity. This data must be clean so we can make predictions on who is likely to use Term-Deposits Need For Clean Data
POWER BI To clean data in Power BI, we use the Power Query Editor Use the “Transform Data” Tab Power Query Editor is a “one-stop-shop” Easy and Intuitive
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