Synthetic Data using Python

  1. Introduction to Synthetic Data
    • Definition and Purpose
    • Importance in Data Privacy
    • Use Cases
  2. Understanding Data Generation Techniques
    • Overview of Data Generation Approaches
    • Parametric vs. Non-Parametric Methods
    • Advantages and Limitations
  3. Python Libraries for Synthetic Data Generation
    • Overview of Popular Libraries
    • Introduction to Faker and Faker Providers
    • Working with NumPy and Pandas for Data Manipulation
  4. Generating Synthetic Numeric Data
    • Random Number Generation
    • Distribution Fitting
    • Correlation in Synthetic Numeric Data
  5. Creating Synthetic Categorical Data
    • Using Faker for Categorical Data
    • Customizing Categories and Labels
    • Handling Imbalanced Data
  6. Synthetic Text Data Generation
    • Text Generation Techniques
    • Introduction to Natural Language Processing (NLP) for Text Data
    • Applications and Challenges
  7. Image and Audio Synthesis using Python
    • Overview of Image Synthesis Techniques
    • Using PIL and OpenCV for Image Generation
    • Generating Synthetic Audio Data with Librosa
  8. Time Series Data Generation
    • Time Series Basics
    • Simulating Temporal Patterns
    • Seasonality and Trends in Synthetic Time Series Data
  9. Evaluation Metrics for Synthetic Data
    • Assessing Data Quality
    • Comparing Synthetic and Real Data
    • Common Evaluation Metrics
  10. Privacy and Ethical Considerations
    • Addressing Privacy Concerns
    • Ethical Use of Synthetic Data
    • Legal Implications
  11. Integration with Machine Learning Workflows
    • Incorporating Synthetic Data into Training Sets
    • Fine-Tuning Models with Synthetic Data
    • Real-world Examples and Case Studies
  12. Challenges and Future Trends
    • Current Challenges in Synthetic Data Generation
    • Emerging Trends and Technologies
    • Future Directions for Synthetic Data in Python
  13. Case Studies
    • Real-world Applications of Synthetic Data
    • Success Stories and Lessons Learned
  14. Conclusion
    • Recap of Key Concepts
    • Encouragement for Further Exploration