Data Processing

Definition Data Processing #

Data processing is the systematic handling of raw data to make it usable for analysis

and decision-making. Key steps include Data Transformation, Data Integration,

and Data Reduction.

Data Transformation #

• Definition: Converting data from one format or structure to another to make it consistent and usable.
• Purpose: Standardize data for analysis.
• Examples:
  • Converting dates (MM/DD/YYYY → YYYY-MM-DD)
  • Normalizing numerical values (0–1 scale)
• Tools/Techniques: Python (pandas), SQL, Excel
• Code Example:

import pandas as pd
df = pd.DataFrame({'date': ['04/02/2026', '03/25/2026']})
df['date'] = pd.to_datetime(df['date'], format='%m/%d/%Y')
print(df)

Data Integration #

• Definition: Combining data from multiple sources into a single, unified dataset.
• Purpose: Provide a complete view of the data for analysis.
• Examples:
  • Merging sales data from multiple regional databases
  • Combining customer info from website and app databases
• Tools/Techniques: SQL (JOIN), Python (merge), ETL pipelines
• Code Example:

df1 = pd.DataFrame({'id':[1,2], 'sales':[100,200]})
df2 = pd.DataFrame({'id':[1,2], 'region':['East','West']})
df_merged = pd.merge(df1, df2, on='id')
print(df_merged)

Data Reduction #

• Definition: Reducing data volume while preserving meaningful information.
• Purpose: Improve efficiency and focus on essential insights.
• Techniques/Examples:
  • Aggregation: Average monthly sales instead of daily data
  • Sampling: Select a representative subset of data
  • Dimensionality Reduction: PCA (Principal Component Analysis) for large feature sets
• Tools/Techniques: Python (pandas, sklearn)
• Code Example (PCA):

from sklearn.decomposition import PCA
import numpy as np

data = np.array([[2,3,5],[3,4,6],[4,5,7]])
pca = PCA(n_components=2)
reduced = pca.fit_transform(data)
print(reduced)