The Statistics and Calculus with Python Workshop

Spis treści książki

• The Statistics and Calculus with Python Workshop
• Preface
  • About the Book
    • Audience
    • About the Chapters
    • Conventions
    • Code Presentation
    • Setting up Your Environment
    • Software Requirements
    • Installation and Setup
    • Installing Python
    • Project Jupyter
    • Installing Libraries
    • Accessing the Code Files
• 1. Fundamentals of Python
  • Introduction
  • Control Flow Methods
    • if Statements
    • Exercise 1.01: Divisibility with Conditionals
    • Loops
      • The while Loop
      • The for Loop
    • Exercise 1.02: Number Guessing Game
  • Data Structures
    • Strings
    • Lists
    • Exercise 1.03: Multi-Dimensional Lists
    • Tuples
    • Sets
    • Dictionaries
    • Exercise 1.04: Shopping Cart Calculations
  • Functions and Algorithms
    • Functions
    • Exercise 1.05: Finding the Maximum
    • Recursion
    • Exercise 1.06: The Tower of Hanoi
    • Algorithm Design
    • Exercise 1.07: The N-Queens Problem
  • Testing, Debugging, and Version Control
    • Testing
    • Debugging
    • Exercise 1.08: Testing for Concurrency
    • Version Control
    • Exercise 1.09: Version Control with Git and GitHub
    • Activity 1.01: Building a Sudoku Solver
  • Summary
• 2. Pythons Main Tools for Statistics
  • Introduction
  • Scientific Computing and NumPy Basics
    • NumPy Arrays
    • Vectorization
    • Exercise 2.01: Timing Vectorized Operations in NumPy
    • Random Sampling
  • Working with Tabular Data in pandas
    • Initializing a DataFrame Object
    • Accessing Rows and Columns
    • Manipulating DataFrames
    • Exercise 2.02: Data Table Manipulation
    • Advanced Pandas Functionalities
    • Exercise 2.03: The Student Dataset
  • Data Visualization with Matplotlib and Seaborn
    • Scatter Plots
    • Line Graphs
    • Bar Graphs
    • Histograms
    • Heatmaps
    • Exercise 2.04: Visualization of Probability Distributions
    • Visualization Shorthand from Seaborn and Pandas
    • Activity 2.01: Analyzing the Communities and Crime Dataset
  • Summary
• 3. Python's Statistical Toolbox
  • Introduction
  • An Overview of Statistics
  • Types of Data in Statistics
    • Categorical Data
    • Exercise 3.01: Visualizing Weather Percentages
    • Numerical Data
    • Exercise 3.02: Min-Max Scaling
    • Ordinal Data
  • Descriptive Statistics
    • Central Tendency
    • Dispersion
    • Exercise 3.03: Visualizing Probability Density Functions
    • Python-Related Descriptive Statistics
  • Inferential Statistics
    • T-Tests
    • Correlation Matrix
    • Exercise 3.04: Identifying and Testing Equality of Means
    • Statistical and Machine Learning Models
    • Exercise 3.05: Model Selection
  • Python's Other Statistics Tools
    • Activity 3.01: Revisiting the Communities and Crimes Dataset
  • Summary
• 4. Functions and Algebra with Python
  • Introduction
  • Functions
    • Common Functions
    • Domain and Range
    • Function Roots and Equations
    • The Plot of a Function
    • Exercise 4.01: Function Identification from Plots
  • Function Transformations
    • Shifts
    • Scaling
    • Exercise 4.02: Function Transformation Identification
  • Equations
    • Algebraic Manipulations
    • Factoring
    • Using Python
    • Exercise 4.03: Introduction to Break-Even Analysis
  • Systems of Equations
    • Systems of Linear Equations
    • Exercise 4.04: Matrix Solution with NumPy
    • Systems of Non-Linear Equations
    • Activity 4.01: Multi-Variable Break-Even Analysis
  • Summary
• 5. More Mathematics with Python
  • Introduction
  • Sequences and Series
    • Arithmetic Sequences
    • Generators
    • Exercise 5.01: Determining the nth Term of an Arithmetic Sequence and Arithmetic Series
    • Geometric Sequences
    • Exercise 5.02: Writing a Function to Find the Next Term of the Sequence
    • Recursive Sequences
    • Exercise 5.03: Creating a Custom Recursive Sequence
  • Trigonometry
    • Basic Trigonometric Functions
    • Exercise 5.04: Plotting a Right-Angled Triangle
    • Inverse Trigonometric Functions
    • Exercise 5.05: Finding the Shortest Way to the Treasure Using Inverse Trigonometric Functions
    • Exercise 5.06: Finding the Optimal Distance from an Object
  • Vectors
    • Vector Operations
    • Exercise 5.07: Visualizing Vectors
  • Complex Numbers
    • Basic Definitions of Complex Numbers
    • Polar Representation and Euler's Formula
    • Exercise 5.08: Conditional Multiplication of Complex Numbers
    • Activity 5.01: Calculating Your Retirement Plan Using Series
  • Summary
• 6. Matrices and Markov Chains with Python
  • Introduction
  • Matrix Operations on a Single Matrix
    • Basic Operations on a Matrix
    • Inspecting a Matrix
    • Exercise 6.01: Calculating the Time Taken for Sunlight to Reach Earth Each Day
    • Operations and Multiplication in Matrices
    • Axes in a Matrix
    • Exercise 6.02: Matrix Search
    • Multiple Matrices
      • Broadcasting
  • Operations on Multiple Matrices
    • Identity Matrix
    • The eye Function
    • Inverse of a Matrix
    • Logical Operators
    • Outer Function or Vector Product
  • Solving Linear Equations Using Matrices
    • Exercise 6.03: Use of Matrices in Performing Linear Equations
  • Transition Matrix and Markov Chains
    • Fundamentals of Markov Chains
      • Stochastic versus Deterministic Models
      • Transition State Diagrams
      • Transition Matrices
    • Exercise 6.04: Finding the Probability of State Transitions
      • Markov Chains and Markov Property
    • Activity 6.01: Building a Text Predictor Using a Markov Chain
  • Summary
• 7. Doing Basic Statistics with Python
  • Introduction
  • Data Preparation
    • Introducing the Dataset
    • Introducing the Business Problem
    • Preparing the Dataset
    • Exercise 7.01: Using a String Column to Produce a Numerical Column
  • Calculating and Using Descriptive Statistics
    • The Need for Descriptive Statistics
    • A Brief Refresher of Statistical Concepts
    • Using Descriptive Statistics
    • Exercise 7.02: Calculating Descriptive Statistics
  • Exploratory Data Analysis
    • What Is EDA?
    • Univariate EDA
    • Bi-variate EDA: Exploring Relationships Between Variables
    • Exercise 7.03: Practicing EDA
    • Activity 7.01: Finding Out Highly Rated Strategy Games
  • Summary
• 8. Foundational Probability Concepts and Their Applications
  • Introduction
  • Randomness, Probability, and Random Variables
    • Randomness and Probability
    • Foundational Probability Concepts
    • Introduction to Simulations with NumPy
    • Exercise 8.01: Sampling with and without Replacement
    • Probability as a Relative Frequency
    • Defining Random Variables
    • Exercise 8.02: Calculating the Average Wins in Roulette
  • Discrete Random Variables
    • Defining Discrete Random Variables
    • The Binomial Distribution
    • Exercise 8.03: Checking If a Random Variable Follows a Binomial Distribution
  • Continuous Random Variables
    • Defining Continuous Random Variables
    • The Normal Distribution
    • Some Properties of the Normal Distribution
    • Exercise 8.04: Using the Normal Distribution in Education
    • Activity 8.01: Using the Normal Distribution in Finance
  • Summary
• 9. Intermediate Statistics with Python
  • Introduction
  • Law of Large Numbers
    • Python and Random Numbers
    • Exercise 9.01: The Law of Large Numbers in Action
    • Exercise 9.02: Coin Flipping Average over Time
    • A Practical Application of the Law of Large Numbers Seen in the Real World
    • Exercise 9.03: Calculating the Average Winnings for a Game of Roulette If We Constantly Bet on Red
  • Central Limit Theorem
    • Normal Distribution and the CLT
    • Random Sampling from a Uniform Distribution
    • Exercise 9.04: Showing the Sample Mean for a Uniform Distribution
    • Random Sampling from an Exponential Distribution
    • Exercise 9.05: Taking a Sample from an Exponential Distribution
  • Confidence Intervals
    • Calculating the Confidence Interval of a Sample Mean
    • Exercise 9.06: Finding the Confidence Interval of Polling Figures
    • Small Sample Confidence Interval
    • Confidence Interval for a Proportion
  • Hypothesis Testing
    • Parts of a Hypothesis Test
    • The Z-Test
    • Exercise 9.07: The Z-Test in Action
    • Proportional Z-Test
    • The T-Test
    • Exercise 9.08: The T-Test
    • 2-Sample T-Test 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
  • Introduction
  • Writing the Derivative Function
    • Exercise 10.01: Finding the Derivatives of Other Functions
    • Finding the Equation of the Tangent Line
  • Calculating Integrals
  • Using Trapezoids
    • Exercise 10.02: Finding the Area Under a Curve
  • Using Integrals to Solve Applied Problems
    • Exercise 10.03: Finding the Volume of a Solid of Revolution
  • Using Derivatives to Solve Optimization Problems
    • Exercise 10.04: Find the Quickest Route
    • Exercise 10.05: The Box Problem
    • Exercise 10.06: The Optimal Can
    • Exercise 10.07: Calculating the Distance between Two Moving Ships
    • Activity 10.01: Maximum Circle-to-Cone Volume
  • Summary
• 11. More Calculus with Python
  • Introduction
  • Length of a Curve
    • Exercise 11.01: Finding the Length of a Curve
    • Exercise 11.02: Finding the Length of a Sine Wave
  • Length of a Spiral
    • Exercise 11.03: Finding the Length of the Polar Spiral Curve
    • Exercise 11.04: Finding the Length of Insulation in a Roll
    • Exercise 11.05: Finding the Length of an Archimedean Spiral
  • Area of a Surface
    • The Formulas
    • Exercise 11.06: Finding the Area of a 3D Surface Part 1
    • Exercise 11.07: Finding the Area of a 3D Surface Part 2
    • Exercise 11.08: Finding the Area of a Surface Part 3
  • Infinite Series
    • Polynomial Functions
    • Series
    • Convergence
    • Exercise 11.09: Calculating 10 Correct Digits of
    • Exercise 11.10: Calculating the Value of 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
    • Exercise 12.01: Calculating Interest
    • Exercise 12.02: Calculating Compound Interest Part 1
    • Exercise 12.03: Calculating Compound Interest Part 2
    • Exercise 12.04: Calculating Compound Interest Part 3
    • Exercise 12.05: Becoming a Millionaire
  • Population Growth
    • Exercise 12.06: Calculating the Population Growth Rate Part 1
    • Exercise 12.07: Calculating the Population Growth Rate Part 2
  • Half-Life of Radioactive Materials
    • Exercise 12.08: Measuring Radioactive Decay
    • Exercise 12.09: Measuring the Age of a Historical Artifact
  • Newton's Law of Cooling
    • Exercise 12.10: Calculating the Time of Death
    • Exercise 12.11: Calculating the Rate of Change in Temperature
  • Mixture Problems
    • Exercise 12.12: Solving Mixture Problems Part 1
    • Exercise 12.13: Solving Mixture Problems Part 2
    • Exercise 12.14: Solving Mixture Problems Part 3
    • Exercise 12.15: Solving Mixture Problems Part 4
  • Euler's Method
    • Exercise 12.16: Solving Differential Equations with Euler's Method
    • Exercise 12.17: Using Euler's Method to Evaluate a Function
    • Runge-Kutta Method
    • Exercise 12.18: Implementing the Runge-Kutta 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
    • Activity 1.01: Building a Sudoku Solver
  • 2. Python's Main Tools for Statistics
    • Activity 2.01: Analyzing the Communities and Crime Dataset
  • 3. Python's Statistical Toolbox
    • Activity 3.01: Revisiting the Communities and Crimes Dataset
  • 4. Functions and Algebra with Python
    • Activity 4.01: Multi-Variable Break-Even Analysis
  • 5. More Mathematics with Python
    • Activity 5.01: Calculating Your Retirement Plan Using Series
  • 6. Matrices and Markov Chains with Python
    • Activity 6.01: Building a Text Predictor Using a Markov Chain
  • 7. Doing Basic Statistics with Python
    • Activity 7.01: Finding Out Highly Rated Strategy Games
  • 8. Foundational Probability Concepts and Their Applications
    • Activity 8.01: Using the Normal Distribution in Finance
  • 9. Intermediate Statistics with Python
    • Activity 9.01: Standardized Test Performance
  • 10. Foundational Calculus with Python
    • Activity 10.01: Maximum Circle-to-Cone Volume
  • 11. More Calculus with Python
    • Activity 11.01: Finding the Minimum of a Surface
  • 12. Intermediate Calculus with Python
    • Activity 12.01: Finding the Velocity and Location of a Particle

pokaż cały spis treści

ukryj recenzje