Part 1: Foundations

1. Chapter 1: What SciPy Is
2. Chapter 2: Installing and Importing SciPy
3. Chapter 3: How SciPy Works with NumPy
4. Chapter 4: SciPy Subpackages
5. Chapter 5: Constants
6. Chapter 6: Special Functions Concepts
7. Chapter 7: Numerical Integration
8. Chapter 8: Integrating a Simple Function
9. Chapter 9: Double Integration Concepts
10. Chapter 10: Numerical Differentiation Concepts

Part 2: Core Operations

1. Chapter 11: Optimization Basics
2. Chapter 12: Minimizing a Function
3. Chapter 13: Bounds in Optimization
4. Chapter 14: Constraints Concepts
5. Chapter 15: Root Finding
6. Chapter 16: Solving Scalar Equations
7. Chapter 17: Solving Systems Concepts
8. Chapter 18: Curve Fitting
9. Chapter 19: Least Squares Concepts
10. Chapter 20: Optimization for Model Parameters

Part 3: Data Analysis

1. Chapter 21: Statistics with SciPy
2. Chapter 22: Descriptive Statistics
3. Chapter 23: Probability Distributions
4. Chapter 24: Normal Distribution
5. Chapter 25: Binomial Distribution
6. Chapter 26: Sampling from Distributions
7. Chapter 27: Probability Density Concepts
8. Chapter 28: Cumulative Distribution Concepts
9. Chapter 29: Percent Point Functions Concepts
10. Chapter 30: Hypothesis Testing

Part 4: Advanced Techniques

1. Chapter 31: T-Test Concepts
2. Chapter 32: Chi-Square Concepts
3. Chapter 33: Correlation Tests Concepts
4. Chapter 34: ANOVA Concepts
5. Chapter 35: Confidence Intervals Concepts
6. Chapter 36: Distance Functions
7. Chapter 37: Euclidean Distance
8. Chapter 38: Cosine Distance
9. Chapter 39: Pairwise Distances Concepts
10. Chapter 40: Clustering Support Concepts

Part 5: AI Workflows

1. Chapter 41: Sparse Matrices
2. Chapter 42: Creating Sparse Matrices
3. Chapter 43: Sparse Matrix Operations
4. Chapter 44: Signal Processing Basics
5. Chapter 45: Filtering Signals
6. Chapter 46: Smoothing Data
7. Chapter 47: Finding Peaks
8. Chapter 48: Frequency Analysis Concepts
9. Chapter 49: Fast Fourier Transform Concepts
10. Chapter 50: Image Processing Concepts

Part 6: Projects and Review

1. Chapter 51: Interpolation Basics
2. Chapter 52: One-Dimensional Interpolation
3. Chapter 53: Multi-Dimensional Interpolation Concepts
4. Chapter 54: Linear Algebra with SciPy
5. Chapter 55: Solving Linear Systems
6. Chapter 56: Matrix Decomposition Concepts
7. Chapter 57: Mini Project: Signal Smoother
8. Chapter 58: Mini Project: Curve Fitting
9. Chapter 59: Mini Project: Statistical Test
10. Chapter 60: Capstone: Scientific AI Support Pipeline