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.