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 high-level 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 real-world 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 Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.

Publisher resources

1. The Statistics and Calculus with Python Workshop
2. Preface
3. 1. Fundamentals of Python
1. Introduction
2. Control Flow Methods
1. if Statements
2. Exercise 1.01: Divisibility with Conditionals
3. Loops
4. Exercise 1.02: Number Guessing Game
3. Data Structures
4. Functions and Algorithms
5. Testing, Debugging, and Version Control
6. Summary
4. 2. Python's Main Tools for Statistics
1. Introduction
2. Scientific Computing and NumPy Basics
3. Working with Tabular Data in pandas
4. Data Visualization with Matplotlib and Seaborn
5. Summary
5. 3. Python's Statistical Toolbox
1. Introduction
2. An Overview of Statistics
3. Types of Data in Statistics
4. Descriptive Statistics
5. Inferential Statistics
6. Python's Other Statistics Tools
7. Summary
6. 4. Functions and Algebra with Python
1. Introduction
2. Functions
3. Function Transformations
4. Equations
5. Systems of Equations
6. Summary
7. 5. More Mathematics with Python
1. Introduction
2. Sequences and Series
3. Trigonometry
4. Vectors
5. Complex Numbers
6. Summary
8. 6. Matrices and Markov Chains with Python
1. Introduction
2. Matrix Operations on a Single Matrix
3. Operations on Multiple Matrices
4. Solving Linear Equations Using Matrices
5. Transition Matrix and Markov Chains
1. Fundamentals of Markov Chains
2. Exercise 6.04: Finding the Probability of State Transitions
3. Activity 6.01: Building a Text Predictor Using a Markov Chain
6. Summary
9. 7. Doing Basic Statistics with Python
1. Introduction
2. Data Preparation
3. Calculating and Using Descriptive Statistics
4. Exploratory Data Analysis
5. Summary
10. 8. Foundational Probability Concepts and Their Applications
1. Introduction
2. Randomness, Probability, and Random Variables
3. Discrete Random Variables
4. Continuous Random Variables
5. Summary
11. 9. Intermediate Statistics with Python
1. Introduction
2. Law of Large Numbers
3. Central Limit Theorem
4. Confidence Intervals
5. Hypothesis Testing
6. Summary
12. 10. Foundational Calculus with Python
1. Introduction
2. Writing the Derivative Function
3. Calculating Integrals
4. Using Trapezoids
5. Using Integrals to Solve Applied Problems
6. Using Derivatives to Solve Optimization Problems
7. Summary
13. 11. More Calculus with Python
1. Introduction
2. Length of a Curve
3. Length of a Spiral
4. Area of a Surface
5. Infinite Series
6. Summary
14. 12. Intermediate Calculus with Python
1. Introduction
2. Differential Equations
3. Interest Calculations
4. Population Growth
6. Newton's Law of Cooling
7. Mixture Problems
8. Euler's Method
9. Summary
15. Appendix
1. 1. Fundamentals of Python
2. 2. Python's Main Tools for Statistics
3. 3. Python's Statistical Toolbox
4. 4. Functions and Algebra with Python
5. 5. More Mathematics with Python
6. 6. Matrices and Markov Chains with Python
7. 7. Doing Basic Statistics with Python
8. 8. Foundational Probability Concepts and Their Applications
9. 9. Intermediate Statistics with Python
10. 10. Foundational Calculus with Python
11. 11. More Calculus with Python
12. 12. Intermediate Calculus with Python

Product information

• Title: The Statistics and Calculus with Python Workshop
• Author(s): Peter Farrell, Alvaro Fuentes, Ajinkya Sudhir Kolhe, Quan Nguyen, Alexander Joseph Sarver, Marios Tsatsos
• Release date: August 2020
• Publisher(s): Packt Publishing
• ISBN: 9781800209763