Book description
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy
Key Features
 Discover how most programmers use the main Python libraries when performing statistics with Python
 Use descriptive statistics and visualizations to answer business and scientific questions
 Solve complicated calculus problems, such as arc length and solids of revolution using derivatives and integrals
Book Description
Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.
The book begins by giving you a highlevel overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.
By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.
What you will learn
 Get to grips with the fundamental mathematical functions in Python
 Perform calculations on tabular datasets using pandas
 Understand the differences between polynomials, rational functions, exponential functions, and trigonometric functions
 Use algebra techniques for solving systems of equations
 Solve realworld problems with probability
 Solve optimization problems with derivatives and integrals
Who this book is for
If you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and RungeKutta methods as the book only explains how these techniques and concepts can be implemented in Python.
Table of contents
 The Statistics and Calculus with Python Workshop
 Preface
 1. Fundamentals of Python
 2. Python's Main Tools for Statistics
 3. Python's Statistical Toolbox
 4. Functions and Algebra with Python
 5. More Mathematics with Python
 6. Matrices and Markov Chains with Python
 7. Doing Basic Statistics with Python
 8. Foundational Probability Concepts and Their Applications

9. Intermediate Statistics with Python
 Introduction
 Law of Large Numbers
 Central Limit Theorem
 Confidence Intervals

Hypothesis Testing
 Parts of a Hypothesis Test
 The ZTest
 Exercise 9.07: The ZTest in Action
 Proportional ZTest
 The TTest
 Exercise 9.08: The TTest
 2Sample TTest or A/B Testing
 Exercise 9.09: A/B Testing Example
 Introduction to Linear Regression
 Exercise 9.10: Linear Regression
 Activity 9.01: Standardized Test Performance
 Summary
 10. Foundational Calculus with Python

11. More Calculus with Python
 Introduction
 Length of a Curve
 Length of a Spiral
 Area of a Surface

Infinite Series
 Polynomial Functions
 Series
 Convergence
 Exercise 11.09: Calculating 10 Correct Digits of π
 Exercise 11.10: Calculating the Value of π Using Euler's Expression
 A 20th Century Formula
 Interval of Convergence
 Exercise 11.11: Determining the Interval of Convergence – Part 1
 Exercise 11.12: Determining the Interval of Convergence – Part 2
 Exercise 11.13: Finding the Constant
 Activity 11.01: Finding the Minimum of a Surface
 Summary

12. Intermediate Calculus with Python
 Introduction
 Differential Equations
 Interest Calculations
 Population Growth
 HalfLife of Radioactive Materials
 Newton's Law of Cooling
 Mixture Problems

Euler's Method
 Exercise 12.16: Solving Differential Equations with Euler's Method
 Exercise 12.17: Using Euler's Method to Evaluate a Function
 RungeKutta Method
 Exercise 12.18: Implementing the RungeKutta Method
 Pursuit Curves
 Exercise 12.19: Finding Where the Predator Catches the Prey
 Exercise 12.20: Using Turtles to Visualize Pursuit Curves
 Position, Velocity, and Acceleration
 Exercise 12.21: Calculating the Height of a Projectile above the Ground
 An Example of Calculating the Height of a Projectile with Air Resistance
 Exercise 12.22: Calculating the Terminal Velocity
 Activity 12.01: Finding the Velocity and Location of a Particle
 Summary

Appendix
 1. Fundamentals of Python
 2. Python's Main Tools for Statistics
 3. Python's Statistical Toolbox
 4. Functions and Algebra with Python
 5. More Mathematics with Python
 6. Matrices and Markov Chains with Python
 7. Doing Basic Statistics with Python
 8. Foundational Probability Concepts and Their Applications
 9. Intermediate Statistics with Python
 10. Foundational Calculus with Python
 11. More Calculus with Python
 12. Intermediate Calculus with Python
Product information
 Title: The Statistics and Calculus with Python Workshop
 Author(s):
 Release date: August 2020
 Publisher(s): Packt Publishing
 ISBN: 9781800209763
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