Data transformation is a critical step in preprocessing data for analysis or integration. Below are common techniques and their applications:

1. Normalization

  • Scales data to a standard range (e.g., 0-1)
  • Useful for algorithms sensitive to magnitude
  • Example: Min-Max Scaling
Normalization

2. Data Encryption

  • Secures sensitive data during transmission/storage
  • Common methods: AES, RSA, TLS
  • Always use secure protocols for data protection
Data_Encryption

3. Filtering

  • Removes irrelevant or noisy data
  • Techniques: Low-pass filters, outlier removal
  • Enhances data quality for downstream tasks
Filtering

4. Aggregation

  • Combines data points into summarized statistics
  • Example: Average, sum, or count operations
  • Reduces complexity for high-level analysis
Aggregation

5. Encoding

  • Converts categorical data into numerical formats
  • Methods: One-hot encoding, label encoding
  • Essential for machine learning pipelines
Encoding

For deeper insights, explore our Data Processing Overview or Data Cleaning Techniques. 📊✨