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دانلود کتاب The Statistics and Calculus with Python Workshop

دانلود کتاب کارگاه آمار و حساب با پایتون

The Statistics and Calculus with Python Workshop

مشخصات کتاب

The Statistics and Calculus with Python Workshop

ویرایش:  
نویسندگان: , , , , ,   
سری:  
ISBN (شابک) : 9781800209763 
ناشر: Packt Publishing Pvt. Ltd. 
سال نشر: 2020 
تعداد صفحات: 0 
زبان: English 
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 19 مگابایت 

قیمت کتاب (تومان) : 38,000



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توضیحاتی در مورد کتاب کارگاه آمار و حساب با پایتون

با مثال‌ها و فعالیت‌هایی که به شما در دستیابی به نتایج واقعی کمک می‌کند، استفاده از حساب دیفرانسیل و انتگرال و روش‌های آماری مرتبط با علم داده پیشرفته هرگز به این سادگی نبوده است. و سوالات علمی حل مسائل پیچیده حساب، مانند طول قوس و جامدات چرخش با استفاده از مشتقات و انتگرال ها شرح کتاب آیا به دنبال شروع توسعه برنامه های کاربردی هوش مصنوعی هستید؟ آیا به تجدید نظر در مفاهیم کلیدی ریاضی نیاز دارید؟ پر از تمرین‌های عملی جذاب، کارگاه آمار و حساب دیفرانسیل و انتگرال با پایتون به شما نشان می‌دهد که چگونه درک خود را از ریاضیات پیشرفته در زمینه پایتون به کار ببرید. این کتاب با ارائه یک نمای کلی در سطح بالا از کتابخانه‌هایی که هنگام انجام آمار با پایتون استفاده می‌کنید، آغاز می‌شود. همانطور که پیشرفت می کنید، کارهای ریاضی مختلفی را با استفاده از زبان برنامه نویسی پایتون انجام خواهید داد، مانند حل توابع جبری با پایتون که با توابع اصلی شروع می شود و سپس از طریق تبدیل ها و حل معادلات کار می کنید. فصول بعدی کتاب به مفاهیم آمار و حساب دیفرانسیل و انتگرال و نحوه استفاده از آنها برای حل مسائل و به دست آوردن بینش مفید می پردازد. در نهایت، معادلات دیفرانسیل را با تاکید بر روش های عددی مطالعه خواهید کرد و با الگوریتم هایی آشنا خواهید شد که به طور مستقیم مقادیر توابع را محاسبه می کنند. در پایان این کتاب، شما یاد خواهید گرفت که چگونه از آمارهای اساسی و مفاهیم حساب دیفرانسیل و انتگرال برای توسعه برنامه های کاربردی پایتون قوی استفاده کنید که چالش های تجاری را حل می کنند. آنچه یاد خواهید گرفت آشنایی با توابع ریاضی اساسی در پایتون انجام محاسبات روی مجموعه داده های جدولی با استفاده از پانداها درک تفاوت بین چندجمله ای ها، توابع گویا، توابع نمایی، و توابع مثلثاتی استفاده از تکنیک های جبر برای حل سیستم های معادلات حل مسائل دنیای واقعی با احتمال حل مسائل بهینه سازی با مشتقات و انتگرال ها این کتاب برای چه کسانی است اگر شما یک برنامه نویس پایتون هستید که می خواهید راه حل های هوشمندی ایجاد کنید که مسائل چالش برانگیز تجاری را حل کند، پس این کتاب برای شما مناسب است. برای درک بهتر مفاهیم توضیح داده شده در این کتاب، باید درک کاملی از مفاهیم پیشرفته ریاضی مانند زنجیره های مارکوف، فرمول اویلر و روش های رانگ-کوتا داشته باشید زیرا این کتاب فقط نحوه پیاده سازی این تکنیک ها و مفاهیم را در پایتون توضیح می دهد.


توضیحاتی درمورد کتاب به خارجی

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.



فهرست مطالب

Cover
FM
Copyright
Table of Contents
Preface
Chapter 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
Chapter 2: Python's 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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 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
Chapter 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
Index




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