Chapter 1, Getting to Know the Tools, explains how to install and configure all the packages required to set up and configure an environment dedicated to scientific computing in Python. The chapter considers several different setup options in the three main operating systems available to users: Windows, macOS, and Linux.

Chapter 2, Getting Started with NumPy, presents the essential recipes for efficient use of NumPy, the Python package for numerical computations on which SciPy is based.

Chapter 3, Using Matplotlib to Create Graphs, is a thorough discussion of Matplotlib, the plotting library included with NumPy and SciPy, concentrating on the skills required to display the results of technical computations.

Chapter 4, Data Wrangling with pandas, shows how to use pandas, a powerful package for data handling and analysis in Python.

Chapter 5, Matrices and Linear Algebra, covers performing the various matrix data manipulation techniques such as basic matrix operations, solving linear systems, finding eigenvalues and eigenvectors, calculating the singular value decomposition, and sparse matrix manipulation techniques that are potentially used in recommender systems using SciPy.

Chapter 6, Solving Equations and Optimization, discusses the solutions of numerical equations and systems of equations, as well as the solution of maximization/minimization problems.

Chapter 7, Constants and Special Functions, presents the numerical constants and special functions that are available in SciPy.

Chapter 8, Calculus, Interpolation, and Differential Equations, shows how to solve essential calculus problems, including integration, differentiation, interpolation, and differential equations.

Chapter 9, Statistics and Probability, covers the various statistics and probability measures such as PMF, PDF, CDF, and multivariate Gaussian distributions using SciPy.

Chapter 10, Advanced Computations with SciPy, discusses the advanced computations available in SciPy that are of a more specific nature.