Statistics Every Programmer Needs
暫譯: 每位程式設計師必備的統計學

Sutton, Gary

  • 出版商: Manning
  • 出版日期: 2025-09-09
  • 售價: $2,570
  • 貴賓價: 9.5$2,442
  • 語言: 英文
  • 頁數: 448
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1633436055
  • ISBN-13: 9781633436053
  • 相關分類: Data Science
  • 尚未上市,無法訂購

相關主題

商品描述

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.

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.

Table of Contents

1 Laying the groundwork
2 Exploring probability and counting
3 Exploring probability distributions and conditional probabilities
4 Fitting a linear regression
5 Fitting a logistic regression
6 Fitting a decision tree and a random forest
7 Fitting time series models
8 Transforming data into decisions with linear programming
9 Running Monte Carlo simulations
10 Building and plotting a decision tree
11 Predicting future states with Markov analysis
12 Examining and testing naturally occurring number sequences
13 Managing projects
14 Visualizing quality control

Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

商品描述(中文翻譯)

將統計學應用於 Python!

數據驅動的決策依賴於統計學。每位程式設計師都需要的統計學 介紹了統計和定量方法,幫助您超越「直覺」來進行如預測股價或評估質量控制等任務,並使用 Python 生態系統中的豐富工具進行示例。

每位程式設計師都需要的統計學 將教您如何:

- 應用基礎和進階的統計技術
- 建立預測模型和模擬
- 在約束條件下優化決策
- 以統計嚴謹性解釋和驗證結果
- 使用 Python 實施定量方法

在這本實用指南中,統計專家 Gary Sutton 將這些統計技術背後的理論與基於 Python 的實際應用相結合,提供結構化、可重複和可辯護的方法來應對複雜的決策。詳細註解和可重用的 Python 代碼清單說明每種方法,並提供您可以跟隨的示例以練習您的新技能。

關於技術

無論您是在分析應用性能指標、創建相關的儀表板和報告,還是沉浸在數據密集的編碼項目中,每位程式設計師都需要知道如何將原始數據轉化為可行的見解。統計學和定量分析是每位程式設計師必備的工具,用以澄清不確定性、優化結果並做出明智的選擇。

關於本書

每位程式設計師都需要的統計學 教您如何將統計學應用於作為軟體開發人員所面臨的日常問題。每一章都是一個新的教程。您將使用線性回歸預測超馬拉松時間,使用時間序列模型預測股價,使用馬可夫鏈分析系統可靠性,還有更多。本書強調理論與實際 Python 實施之間的平衡,並提供註解代碼和真實世界的示例,以確保實用理解和跨行業的適應性。

內容概覽

- 機率基礎和分佈
- 隨機變數
- 回歸
- 決策樹和隨機森林
- 時間序列分析
- 線性規劃
- 蒙地卡羅和馬可夫方法等

讀者對象

示例使用 Python。

關於作者

Gary Sutton 是商業智慧和分析領域的領導者,也是《Statistics Slam Dunk: Statistical analysis with R on real NBA data》的作者。

目錄

1 建立基礎
2 探索機率和計數
3 探索機率分佈和條件機率
4 擬合線性回歸
5 擬合邏輯回歸
6 擬合決策樹和隨機森林
7 擬合時間序列模型
8 使用線性規劃將數據轉化為決策
9 執行蒙地卡羅模擬
10 建立和繪製決策樹
11 使用馬可夫分析預測未來狀態
12 檢查和測試自然發生的數字序列
13 管理專案
14 可視化質量控制

購買印刷版書籍時,您將獲得 Manning 提供的免費電子書(PDF 或 ePub)以及在線 liveBook 格式的訪問權限(及其 AI 助手,將以任何語言回答您的問題)。

作者簡介

Gary Sutton is a vice president for a leading financial services company. He has built and led high-performing business intelligence and analytics organizations across multiple verticals, where R was the preferred programming language for predictive modeling, statistical analyses, and other quantitative insights. Gary earned his undergraduate degree from the University of Southern California, a Masters from George Washington University, and a second Masters in Data Science, from Northwestern University.

作者簡介(中文翻譯)

加里·薩頓是某家領先金融服務公司的副總裁。他在多個行業中建立並領導了高效能的商業智慧和分析組織,其中 R 是用於預測建模、統計分析和其他定量洞察的首選程式語言。加里在南加州大學獲得了學士學位,並在喬治華盛頓大學獲得碩士學位,隨後又在西北大學獲得數據科學的第二個碩士學位。