10 Data Cleaning Techniques Every Data Analyst Should Master:

1. Handling Missing Data

Use methods like imputation (mean, median, mode) or deletion to handle missing values.

In Python, pandas functions like fillna() or dropna() are useful.
 
Example: df.fillna(df.mean()) replaces missing values with the column mean.
 
2. Removing Duplicates

Identify and remove duplicate records to ensure the dataset is accurate. Use drop_duplicates() in pandas.