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ویرایش: نویسندگان: Peter Farrell, Alvaro Fuentes, Ajinkya Sudhir Kolhe, Quan Nguyen, Alexander Joseph Sarver, and Marios Tsatsos 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
در صورت تبدیل فایل کتاب The Statistics and Calculus with Python Workshop به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب کارگاه آمار و حساب با پایتون نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
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.
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.
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