1. Introduction
2. 1. Getting Started with Rust for Data Analysis
3. 1. 1.1. Why Rust for Data Analysis?
   2. 1.2. Setting Up Your Environment
   3. 1.3. Essential Crates for Data Analysis
   4. 1.4. Your First Data Analysis Project
4. 2. Working with Data in Rust
5. 1. 2.1. Reading and Writing Data Files
   2. 2.2. CSV and JSON Processing
   3. 2.3. Working with Databases
   4. 2.4. Data Cleaning and Preprocessing
6. 3. Data Structures and Algorithms
7. 1. 3.1. Vectors, Arrays and Matrices
   2. 3.2. DataFrames with Polars
   3. 3.3. Working with ndarray
   4. 3.4. Implementing Custom Data Structures
8. 4. Statistical Analysis
9. 1. 4.1. Descriptive Statistics
   2. 4.2. Probability Distributions
   3. 4.3. Hypothesis Testing
   4. 4.4. Regression Analysis
10. 5. Data Visualization
11. 1. 5.1. Plotting with Plotters
    2. 5.2. Interactive Visualizations
    3. 5.3. Creating Custom Visualizations
    4. 5.4. Exporting Charts and Graphs
12. 6. Machine Learning in Rust
13. 1. 6.1. Machine Learning Ecosystem in Rust
    2. 6.2. Linear Models with linfa
    3. 6.3. Neural Networks with burn
    4. 6.4. Model Evaluation and Validation
14. 7. Performance Optimization
15. 1. 7.1. Benchmarking Your Analysis
    2. 7.2. Parallelism and Concurrency
    3. 7.3. SIMD Operations
    4. 7.4. Memory Optimization
16. 8. Building Data Analysis Applications
17. 1. 8.1. Command-Line Data Tools
    2. 8.2. Web APIs for Data Services
    3. 8.3. Desktop Applications with egui
    4. 8.4. Deploying Data Analysis Code
18. 9. Case Studies
19. 1. 9.1. Financial Data Analysis
    2. 9.2. Scientific Computing
    3. 9.3. Big Data Processing
    4. 9.4. Real-time Data Analysis
20. 10. Future Directions
21. 1. 10.1. Emerging Tools and Libraries
    2. 10.2. Integrating with Python Ecosystem
    3. 10.3. Contributing to the Rust Data Ecosystem
    4. 10.4. Resources for Further Learning