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    Statistics with Rust, Second Edition
    Statistics with Rust, Second Edition
    Statistics with Rust, Second Edition
    Ebook297 pages2 hours

    Statistics with Rust, Second Edition

    By Keiko Nakamura

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    About this ebook

    "Statistics with Rust, Second Edition" is designed to help you learn quickly, focusing on practical statistics using Rust scripts. The book is for readers who know the basics of statistics and machine learning. It gives quick explanations so you can try out concepts with hands-on coding. The book uses the newest version of Rust, 1.72.0, to help users build and secure statistical and machine learning algorithms. Each chapter is full of useful programs and code examples that will walk you through tasks like data manipulation, statistical tests, regression analysis, building machine learning models, and natural language processing.

    This second edition brings all chapters up to date with the latest in stats and Rust programming. It focuses on how you can put these things to practical use, with a detailed look at advanced algorithms like PCA, SVM, neural networks, and ensemble methods. We've also included some natural language processing topics, such as text preprocessing, tokenization, and word embeddings. The book also shows you how to combine Rust's performance and safety with statistical analysis, giving you the tools you need to do data analysis efficiently and reliably.

     

     

    Table of Content

    Introduction to Rust for Statisticians

    Data Handling and Preprocessing

    Descriptive Statistics

    Probability Distributions and Random Variables

    Inferential Statistics

    Regression Analysis

    Bayesian Statistics

    Multivariate Statistical Methods

    Nonlinear Models and Machine Learning

    Model Evaluation and Validation

    Text and Natural Language Processing

    LanguageEnglish
    PublisherGitforGits
    Release dateOct 10, 2024
    ISBN9798230554684
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      Book preview

      Statistics with Rust, Second Edition - Keiko Nakamura

      Statistics with Rust

      Second Edition

      Explore rust programming and its powerful crates across data science, machine learning and NLP projects

      Keiko Nakamura

      Preface

      Statistics with Rust, Second Edition is designed to help you learn quickly, focusing on practical statistics using Rust scripts. The book is for readers who know the basics of statistics and machine learning. It gives quick explanations so you can try out concepts with hands-on coding.

      The book uses the newest version of Rust, 1.72.0, to help users build and secure statistical and machine learning algorithms. Each chapter is full of useful programs and code examples that will walk you through tasks like data manipulation, statistical tests, regression analysis, building machine learning models, and natural language processing.

      We've covered great Rust crates featured throughout, including:

      ndarray and ndarray-linalg: For efficient handling of multi-dimensional arrays and linear algebra operations.

      ndarray-stats: To perform statistical computations on arrays.

      rand and rand_distr: For generating random numbers and working with probability distributions.

      smartcore: A machine learning library used for implementing algorithms like decision trees and random forests.

      linfa: A toolkit providing implementations of Support Vector Machines and other algorithms.

      tch: Rust bindings for PyTorch, enabling the creation and training of neural networks.

      finalfusion: For working with word embeddings in natural language processing tasks.

      rust-stemmers: To perform stemming in text preprocessing.

      regex: For pattern matching and text manipulation.

      unicode-segmentation: To accurately tokenize Unicode strings.

      This second edition brings all chapters up to date with the latest in stats and Rust programming. It focuses on how you can put these things to practical use, with a detailed look at advanced algorithms like PCA, SVM, neural networks, and ensemble methods. We've also included some natural language processing topics, such as text preprocessing, tokenization, and word embeddings.

      The book also shows you how to combine Rust's performance and safety with statistical analysis, giving you the tools you need to do data analysis efficiently and reliably. The book's got lots of practical code and explanations that are easy to understand, which helps you learn the skills you need to get to grips with data using Rust.

      GitforGits

      Prerequisites

      This book is perfect for every data user, including data scientist, NLP engineers, rust programmers, data engineers, data analysts and all those who are knowning simple basics of statistics and eager to use Rust programming for data science and machine learning projects.

      Codes Usage

      Are you in need of some helpful code examples to assist you in your programming and documentation? Look no further! Our book offers a wealth of supplemental material, including code examples and exercises.

      Not only is this book here to aid you in getting your job done, but you have our permission to use the example code in your programs and documentation. However, please note that if you are reproducing a significant portion of the code, we do require you to contact us for permission.

      But don't worry, using several chunks of code from this book in your program or answering a question by citing our book and quoting example code does not require permission. But if you do choose to give credit, an attribution typically includes the title, author, publisher, and ISBN. For example, Statistics with Rust, Second Edition by Keiko Nakamura.

      If you are unsure whether your intended use of the code examples falls under fair use or the permissions outlined above, please do not hesitate to reach out to us at support@gitforgits.com. 

      We are happy to assist and clarify any concerns.

      Prologue

      I set out to write Statistics with Rust with one clear vision: to merge the precision of statistical analysis with the performance and safety of Rust programming. This second edition builds upon the foundation of the first. The journey has been both challenging and rewarding, and I am pleased to present it. Rust has evolved remarkably over the past few years. It is now at version 1.72.0 and has brought enhanced features and a growing ecosystem of libraries. I was inspired to revisit the original content and incorporate the latest advancements in both statistics and Rust. My goal has always been to provide a hands-on experience, and this edition intensifies that focus with more practical programs and real-world examples.

      This edition includes a deeper exploration of machine learning and natural language processing, which I am excited to share. I have added new chapters on nonlinear models, multivariate techniques, and text analysis. You will find implementations of algorithms like Support Vector Machines, Neural Networks, and Principal Component Analysis, all using Rust's powerful crates such as smartcore, linfa, and tch. These examples prove that Rust is the ideal tool for complex data analysis tasks.

      Working with real datasets often presents challenges like handling large volumes of data and ensuring code efficiency. Rust is the ideal choice for tackling these issues thanks to its memory safety and performance. The book will show you how to leverage Rust's features to write robust and efficient code. You'll learn how to manipulate data using ndarray, perform statistical computations with ndarray-stats, and process text data with crates like regex and unicode-segmentation. Furthermore, I have made it a priority to include a strong focus on practical application. Each chapter includes code snippets and full programs that you can run and modify. You will have hands-on projects that reinforce the concepts discussed, whether you're building a regression model, performing hypothesis testing, or working on time series analysis.

      I am especially proud of how this book bridges the gap between theory and practice. Statistics can feel abstract, but I've made them tangible and accessible by implementing the concepts in Rust. I explain not just the how, but also the why behind each method. This helps you develop a deeper understanding of both statistics and programming. Writing this book has been an incredible journey of learning and discovery for me. The Rust community is growing and innovating in ways that continually inspire me. This book will empower you to harness Rust's capabilities in your statistical work and open up new possibilities for analysis and application.

      I'm grateful for the opportunity to share this passion with you. I'm confident you'll apply these tools and concepts in your own projects, and I'm certain we'll continue to make advancements together in the fields of statistics and Rust programming.

      — Keiko Nakamura

      Copyright © 2024 by GitforGits

      All rights reserved. This book is protected under copyright laws and no part of it may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without the prior written permission of the publisher. Any unauthorized reproduction, distribution, or transmission of this work may result in civil and criminal penalties and will be dealt with in the respective jurisdiction at anywhere in India, in accordance with the applicable copyright laws.

      Published by: GitforGits

      Publisher: Sonal Dhandre

      www.gitforgits.com

      support@gitforgits.com

      Printed in India

      First Printing: October 2024

      Cover Design by: Kitten Publishing

      For permission to use material from this book, please contact GitforGits at support@gitforgits.com.

      Content

      Preface

      GitforGits

      Acknowledgement

      Chapter 1: Introduction to Rust for Statisticians

      Overview

      Why Rust for Data Analysis and Statistics?

      High Performance

      Memory Safety and Reliability

      Safe Concurrency

      Interoperability and Ecosystem Integration

      WebAssembly and Cross-Platform Capabilities

      Modern Language for Modern Challenges

      Comparing Rust and Python for Statistics

      Performance Considerations

      Memory Management and Safety

      Concurrency and Parallelism

      Ecosystem and Libraries

      Development Experience

      Scalability and Maintainability

      Deployment and Portability

      Setting up Rust on Linux

      Install Required Dependencies

      Install Rustup

      Configure Environment

      Essential Rust Libraries for Statistics

      ‘Ndarray’ for N-Dimensional Arrays

      ‘Polars’ for DataFrames

      ‘Statrs’ for Statistical Computations

      ‘Plotters’ for Data Visualization

      Setting up Statistical Project

      Create a New Cargo Project

      Add Dependencies

      Build Project

      Install System Dependencies

      Write Code

      Run Project

      Understanding Rust's Ownership Model

      Ownership Rules

      Borrowing

      Lifetimes

      Summary

      Chapter 2: Data Handling and Preprocessing

      Introduction

      Understanding Data Preprocessing

      Why Preprocess Data?

      Process of Data Handling and Preprocessing

      Sample Dataset Overview

      Loading and Parsing Data

      Setting up Project

      Adding Dependencies

      Defining Data Structure

      Fetching Dataset

      Loading and Parsing CSV Data

      Main Function

      Exploring Dataset

      Viewing Sample Records

      Counting Unique Job Titles

      Calculating Average Salary

      Data Cleaning

      Handling Missing Values

      Removing Duplicates

      Correcting Inconsistencies

      Data Transformation

      Scaling Numerical Data

      Encoding Categorical Variables

      Feature Engineering

      Data Splitting

      Final Code Integration

      Summary

      Chapter 3: Descriptive Statistics

      Introduction

      Understanding Descriptive Statistics

      Measures of Central Tendency

      Calculating Mean

      Calculating Median

      Calculating Mode

      Applying All Measures of Central Tendency

      Measures of Dispersion

      Calculating Range

      Calculating Variance

      Calculating Standard Deviation

      Applying All Measures of Dispersion

      Exploratory Data Analysis (EDA)

      EDA Overview

      Visualizing Data with Plotters

      Analyzing Categorical Variables

      Correlation Analysis

      Summarizing Data with Descriptive Statistics

      Sample Summary Table

      Implementing a Summary Function

      Sample Program: Analyzing Our Dataset

      Summary

      Chapter 4: Probability Distributions and Random Variables

      Introduction

      Understanding Random Variables and Probability Distributions

      Random Variables

      Probability Distributions

      Discrete Probability Distributions

      Common Discrete Distributions

      Uniform Distribution

      Bernoulli Distribution

      Binomial Distribution

      Poisson Distribution

      Geometric Distribution

      Continuous Probability Distributions

      Uniform Distribution

      Normal (Gaussian) Distribution

      Exponential Distribution

      Beta Distribution

      Gamma Distribution

      Generating Random Variables

      Sampling from Distributions

      Sampling Benefits

      Sample Program: Sampling from a Normal Distribution

      Estimating Distribution Parameters

      Method of Moments (MoM)

      Maximum Likelihood Estimation (MLE)

      Bayesian Estimation

      Least Squares

      Summary

      Chapter 5: Inferential Statistics

      Introduction

      Fundamentals of Inferential Statistics

      Why Inferential Statistics?

      Key Concepts

      Hypothesis Testing

      Hypothesis Testing Process

      Sample Program: Comparing Salaries

      Performing Hypothesis Testing

      Chi-Square Test for Independence

      Confidence Intervals

      Confidence Interval for Mean

      Confidence Interval for Proportion

      Parametric Tests

      Paired T-Test

      One-Way ANOVA

      Non-Parametric Tests

      Wilcoxon Rank-Sum Test (Mann-Whitney U Test)

      Kruskal-Wallis Test

      Summary

      Chapter 6: Regression Analysis

      Overview

      Introduction to Regression Analysis

      Overview

      Applications of Regression Analysis

      Types of Regression Analysis

      Simple Linear Regression

      Understanding Equation

      Applying Simple Regression

      Multiple Linear Regression

      Understanding Equation

      Applying Multiple Regression

      Polynomial Regression

      Understanding the Equation

      Applying Polynomial Regression

      Logistic Regression

      Understanding Equation

      Applying Logistic Regression

      Summary

      Chapter 7: Bayesian Statistics

      Overview

      Introduction to Bayesian Statistics

      Overview

      Bayes' Theorem

      Advantages of Bayesian Statistics

      Bayesian Inference

      Understanding Bayesian Inference

      Bayesian Inference Procedure

      Putting Bayesian Inference into Action

      Advanced Markov Chain Monte Carlo Methods

      Introduction to Hamiltonian Monte Carlo (HMC)

      Implementing HMC

      Model Comparison and Selection

      Importance of Model Comparison

      Model Comparison using DIC

      Model Comparison using WAIC

      Summary

      Chapter 8: Multivariate Statistical Methods

      Introduction

      Principal Component Analysis (PCA)

      Understanding PCA

      Implementing PCA

      Canonical Correlation Analysis (CCA)

      Understanding CCA

      Putting CCA into Action

      Linear Discriminant Analysis (LDA)

      Understanding LDA

      Performing LDA Algorithm

      Independent Component Analysis (ICA)

      Understanding ICA

      Applying ICA

      Multidimensional Scaling (MDS)

      Understanding MDS

      Implementing MDS

      Summary

      Chapter 9: Nonlinear Models and Machine Learning

      Overview

      Introduction to Nonlinear Models and Machine Learning

      Why Nonlinear Models?

      Machine Learning Breakthroughs

      Decision Trees

      Understanding Decision Trees

      Key Characteristics

      Building a Decision Tree

      Implementing Decision Trees

      Support Vector Machines (SVM)

      Understanding SVM

      Implementing SVM

      Neural Networks

      Understanding Neural Networks

      Implementing Neural Networks

      Ensemble Methods

      Understanding Ensemble Methods

      Implementing Random Forests

      Summary

      Chapter 10: Model Evaluation and Validation

      Introduction

      The Importance of Model Evaluation and Validation

      Why Model Evaluation?

      Common Evaluation Techniques

      Train-Test Split

      Understanding Train-Test Split

      Implementing Train-Test Split

      Cross-Validation Technique

      Understanding Cross-Validation

      Implementing K-Fold Cross-Validation

      Hyperparameter Tuning

      Understanding Hyperparameter Tuning

      Implementing Grid Search

      Model Selection Techniques

      Understanding AIC and BIC

      Implementing AIC and BIC

      Resampling Methods

      Understanding Resampling Methods

      Implementing Bootstrapping and Permutation Tests

      Summary

      Chapter 11: Text and Natural Language Processing

      Introduction

      Overview of Natural Language Processing

      Historical Development of NLP

      Adoption and Applications of NLP

      Key Processes in Natural Language Processing

      Tokenization

      Stopword Removal

      Stemming and Lemmatization

      Part-of-Speech (POS) Tagging

      Named Entity Recognition (NER)

      Dependency Parsing

      Sentiment Analysis

      Machine Translation

      Text Summarization

      Text Preprocessing and Tokenization

      Key Preprocessing Techniques

      Tokenization Approaches

      Implementing Text Preprocessing and Tokenization

      Implement Stopword Removal

      Stemming and Lemmatization

      Understanding Stemming and Lemmatization

      Implementing Stemming

      Information Retrieval with TF-IDF

      Understanding TF-IDF

      Implementing TF-IDF

      Word Embeddings and Word2Vec

      Understanding Word Embeddings

      Implementing Word Embeddings

      Summary

      Index

      Epilogue

      Chapter 1: Introduction to Rust for Statisticians

      Overview

      In this first chapter, I will introduce you to the fascinating world of statistics and Rust

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