Understand the Data:
Begin by understanding the structure and content of the dataset.
Check the format (CSV, Excel, JSON, etc.), size, and the number of records.
Identify the columns and their data types.
Data Cleaning:
Handle Missing Values:
Identify missing values in the dataset.
Decide how to deal with missing values (remove, impute, etc.).
Data Formatting:
Check for inconsistencies in formatting (e.g., date formats, capitalization).
Standardize formatting if necessary.
Remove Duplicates:
Check for duplicate records and remove them if needed.
Handle Outliers:
Identify outliers and decide how to handle them (remove, transform, etc.).
Data Transformation:
Perform any necessary transformations (logarithmic, normalization, etc.).
Exploratory Data Analysis (EDA):
Descriptive Statistics:
Compute basic statistics (mean, median, mode, etc.) for numerical variables.
Generate frequency tables for categorical variables.