Название: Statistics Every Programmer Needs (Final Release)Автор: Gary Sutton
Издательство: Manning Publications
Год: 2025
Страниц: 450
Язык: английский
Формат: pdf (true)
Размер: 47.8 MB
Put statistics into practice with Python!
Data-driven decisions rely on statistics. Statistics Every Programmer Needs introduces the statistical and quantitative methods that will help you go beyond “gut feeling” for tasks like predicting stock prices or assessing quality control, with examples using the rich tools of the Python ecosystem.
Statistics Every Programmer Needs will teach you how to
• Apply foundational and advanced statistical techniques
• Build predictive models and simulations
• Optimize decisions under constraints
• Interpret and validate results with statistical rigor
• Implement quantitative methods using Python
In this hands-on guide, stats expert Gary Sutton blends the theory behind these statistical techniques with practical Python-based applications, offering structured, reproducible, and defensible methods for tackling complex decisions. Well-annotated and reusable Python code listings illustrate each method, with examples you can follow to practice your new skills.
About the technology
Whether you’re analyzing application performance metrics, creating relevant dashboards and reports, or immersing yourself in a numbers-heavy coding project, every programmer needs to know how to turn raw data into actionable insight. Statistics and quantitative analysis are the essential tools every programmer needs to clarify uncertainty, optimize outcomes, and make informed choices.
About the book
Statistics Every Programmer Needs teaches you how to apply statistics to the everyday problems you’ll face as a software developer. Each chapter is a new tutorial. You’ll predict ultramarathon times using linear regression, forecast stock prices with time series models, analyze system reliability using Markov chains, and much more. The book emphasizes a balance between theory and hands-on Python implementation, with annotated code and real-world examples to ensure practical understanding and adaptability across industries.
The idea for this book took shape as I noticed the increasing demand for statistical and machine learning techniques in business, finance, and engineering. Companies were hiring data scientists and analysts in record numbers, but many professionals found themselves needing to apply advanced methods without a structured way to learn them. More and more, I have seen practitioners who can write Python scripts and present results to leadership but lack a deep understanding of what is happening under the hood. This superficial knowledge can lead to misinterpretations, poor model assumptions, and flawed decision-making. Knowing how to apply statistical methods is important—but understanding when, why, and under what conditions they work is critical.
The book covers a range of topics essential for any data-driven professional, beginning with foundational probability theory and moving through regression analysis, decision trees, Monte Carlo simulations, and Markov chains. Later chapters explore project management and quality control—areas where quantitative methods play a crucial role in ensuring efficiency and reliability. Although Python is used throughout the book as a computational tool, this is not just a Python book; it is a guide to using quantitative methods effectively, providing reusable code alongside clear explanations to ensure that you understand the concepts behind the calculations. A key focus of this book is demonstrating how these techniques are applied in practice.
What's inside:
• Probability basics and distributions
• Random variables
• Regression
• Decision trees and random forests
• Time series analysis
• Linear programming
• Monte Carlo and Markov methods and much more
About the reader:
Examples are in Python.
About the author:
Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Dunk: Statistical analysis with R on real NBA data.
Contents:
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