8 Effective Data Cleaning Techniques for Data Analytics
8 Effective Data Cleaning Techniques for Data Analytics
Why Is Data Cleaning so Important?
Why Is Data Cleaning so Important?
Having clean data will ultimately
increase overall productivity and allow for the highest quality information in your decision-making
Having clean data will ultimately
increase overall productivity and allow for the highest quality information in your decision-making
Data Cleaning Techniques 1. Remove duplicates 2. Remove irrelevant data 3. Standardize capitalization 4. Convert data type 5. Clear formatting 6. Fix errors 7. Language translation 8. Handle missing values
Data Cleaning Techniques
1. Remove duplicates
2. Remove irrelevant data
3. Standardize capitalization
4. Convert data type
5. Clear formatting
6. Fix errors
7. Language translation
8. Handle missing values
These duplicates could originate from human error where the person inputting the data or filling out a form made a mistake.
These duplicates could originate from human error where the person inputting the data or filling out a form made a mistake.
Remove Duplicates
2. Remove Irrelevant Data
2. Remove Irrelevant Data
Irrelevant data will slow down and confuse any analysis that you want to do
Irrelevant data will slow down and confuse any analysis that you want to do
3. Standardize Capitalization
3. Standardize Capitalization
These duplicates could originate from human error where the person inputting the data or filling out a form made a mistake.
These duplicates could originate from human error where the person inputting the data or filling out a form made a mistake.
4. Convert Data Types
4. Convert Data Types
Numbers are the most common data type that you will need to convert when cleaning your data.
Numbers are the most common data type that you will need to convert when cleaning your data.
5. Clear Formatting
5. Clear Formatting
You should remove any kind of formatting that has been applied to your documents
You should remove any kind of formatting that has been applied to your documents
6. Fix Errors
6. Fix Errors
Errors as avoidable as typos could lead to you missing out on key findings from your data.
Errors as avoidable as typos could lead to you missing out on key findings from your data.
7. Language Translation
7. Language Translation
To have consistent data, you’ll want everything in the same language.
To have consistent data, you’ll want everything in the same language.
8. Handle Missing Values
8. Handle Missing Values
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Remove the observations that have this missing value
–
Input the missing data
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Remove the observations that have this missing value
–
Input the missing data
Data Analytics Training and Placement
Data Analytics Training and Placement