UNDERSTAND THE DATA
•FamiliarizeYourself with the Dataset: Understand the
structure, content, and purpose of the data before making any
changes.
•Identify Key Columns: Recognize which columns are
essential for your analysis and focus on ensuring their accuracy.
4.
REMOVE UNNECESSARY DATA
•FilterOut Irrelevant Rows: Remove rows that do not
contribute to your analysis, such as test data, duplicates, or
outliers.
•Remove Unused Columns: Delete columns that are not
needed to streamline the dataset and improve performance.
5.
HANDLE MISING DATA
•IdentifyMissing Values: Look for nulls, blanks, or errors
that indicate missing data.
•Choose a Strategy: Decide how to handle missing data—
whether by replacing it (with mean, median, mode, etc.),
filling it forward/backward, or removing the affected rows or
columns.
6.
STANDARDIZE DATA FORMAT
•ConsistentData Types: Ensure that each column is set to the correct data
type (e.g., text, number, date). This helps prevent calculation errors and
improves data accuracy.
•Standardize Date Formats: Ensure that date formats are consistent across the
dataset, considering regional settings if necessary.
•Normalize Text Data: Standardize text data by converting to lower or upper
case, removing extra spaces, and ensuring consistent formatting (e.g., phone
numbers, addresses).
7.
CORRECT ERRORS ANDINCONSISTENCIES
•Identify and Correct Errors: Use tools like Replace
Values to correct misspellings, incorrect values, or
inconsistent entries.
•Address Inconsistent Data: Standardize entries that
should be uniform (e.g., "NY" vs. "New York").
8.
TRANSFORM AND RE-SHAPEDATA
•Pivot/Unpivot: Use pivot and unpivot operations to restructure the data as
needed for your analysis.
•Split Columns: Break down columns with multiple pieces of information into
separate columns (e.g., splitting full names into first and last names).
•Merge Columns: Combine columns if necessary, such as merging first and
last names into a single column.
9.
REMOVE DUPLICATES
•Remove Duplicates:Identify and remove duplicate rows to
ensure each record is unique.
•Identify Key Columns for Uniqueness: Use unique identifiers
or combinations of columns to determine duplicates accurately.
10.
DATA VALIDATION
•Cross-Check Data:Validate cleaned data against the original
source to ensure accuracy.
•Check for Anomalies: Look for unexpected changes or trends
after cleaning to catch any errors introduced during the
process.
