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