1. Introduction to Data Transformation and Normalization:
Understanding the importance of data preprocessing. The role of data transformation and normalization in preparing data for analysis. 2. Data Cleaning (Review):
A brief review of data cleaning techniques, including handling missing values and duplicates. 3. Data Transformation Techniques:
3.1. Encoding Categorical Data:
One-Hot Encoding. Label Encoding. Custom encoding for ordinal data. 3.2. Feature Scaling:
Min-Max Scaling (Normalization). Standardization (Z-score normalization). Robust Scaling. 3.3. Log Transformation:
When and why to use log transformation for skewed data. 3.4. Binning and Discretization:
Grouping continuous data into bins or categories. Use cases for discretization. 4. Handling Outliers:
Identifying outliers. Techniques for handling outliers, such as truncation or winsorization. 5. Data Imputation (Review):
Review imputation techniques for handling missing data, including mean, median, and more advanced methods. 6. Feature Engineering:
Techniques for creating new features from existing ones. Feature scaling after feature engineering. 7. Time Series Data Transformation (if applicable):
Resampling time series data. Lag features. Rolling statistics. 8. Normalization Techniques:
8.1. Min-Max Normalization:
Scaling data to a specific range (e.g., [0, 1]). 8.2. Z-Score (Standard) Normalization:
Scaling data to have a mean of 0 and standard deviation of 1. 8.3. Robust Normalization:
Normalizing data using median and interquartile range (IQR). 9. Handling Skewed Data:
Identifying and measuring skewness in data. Applying transformations to make data more symmetric (e.g., Box-Cox transformation). 10. Data Transformation and Normalization Libraries in Python: - Introduction to Python libraries like scikit-learn and pandas for performing data transformation and normalization.
11. Best Practices: - Guidelines for when to use specific techniques. - Avoiding common pitfalls in data preprocessing.
12. Evaluation and Validation: - How data transformation and normalization affect the performance of machine learning models. - Cross-validation and assessing model performance.
13. Real-world Applications: - Practical examples and case studies demonstrating the importance of data transformation and normalization in real-world datasets.
14. Hands-on Exercises and Projects: - Practical exercises and projects to reinforce the concepts learned throughout the course.
15. Performance Optimization: - Techniques for optimizing the performance of data preprocessing pipelines, especially for large datasets.
16. Integration with Machine Learning Pipelines (Optional): - How to integrate data transformation and normalization into machine learning workflows.
17. Ethical Considerations: - Addressing ethical issues related to data preprocessing, including biases introduced by normalization.