#### Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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# Technical requirements

In this chapter, as usual, we will need the NumPy package imported under the alias np, the Matplotlib pyplot module imported as plt, and the Pandas package imported as pd. We can do this using the following commands:

`import numpy as npimport matplotlib.pyplot as pltimport pandas as pd`

We will also need some new packages in this chapter. The statsmodels package is used for regression and time series analysis, the scikit-learn package (sklearn) provides general data science and machine learning tools, and the Prophet package (fbprophet) is used for automatically modeling time series data. These packages can be installed using your favorite package manager, such as pip:

```          python3.8 -m pip install statsmodels sklearn fbprophet
```

The Prophet package can prove difficult to install on some operating systems because of its dependencies. If installing fbprophet causes a problem, you might want to try using the Anaconda...