Practical Data Science with R (Paperback)

Nina Zumel, John Mount

Practical Data Science with R (Paperback)

買這商品的人也買了...

相關主題

商品描述

Summary

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Book

Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.

This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.

What's Inside

  • Data science for the business professional
  • Statistical analysis using the R language
  • Project lifecycle, from planning to delivery
  • Numerous instantly familiar use cases
  • Keys to effective data presentations

About the Authors

Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.

Table of Contents

PART 1 INTRODUCTION TO DATA SCIENCE
PART 2 MODELING METHODS
PART 3 DELIVERING RESULTS
  1. The data science process
  2. Loading data into R
  3. Exploring data
  4. Managing data

  5. Choosing and evaluating models
  6. Memorization methods
  7. Linear and logistic regression
  8. Unsupervised methods
  9. Exploring advanced methods

  10. Documentation and deployment
  11. Producing effective presentations

商品描述(中文翻譯)


摘要

使用R進行實用數據科學不負其名。它解釋了基本原理，沒有理論上的废话，直接跳到你在收集、整理和分析對你的業務成功至關重要的數據時會遇到的真實用例。你將應用R編程語言和統計分析技術，通過基於市場營銷、商業智能和決策支持的詳細解釋的示例。

購買印刷版書籍將包括Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於本書

商業分析師和開發人員越來越多地收集、整理、分析和報告關鍵的業務數據。R語言及其相關工具提供了一種直接的方式來應對日常數據科學任務，而不需要太多學術理論或高級數學知識。

使用R進行實用數據科學向您展示如何應用R編程語言和有用的統計技術處理日常業務情境。通過市場營銷、商業智能和決策支持的示例，它向您展示如何設計實驗（例如A/B測試）、構建預測模型並向各級觀眾呈現結果。

本書適合沒有數據科學背景的讀者閱讀。假設讀者對基本統計學、R或其他腳本語言有一定的熟悉。

內容簡介


  • 針對商業專業人士的數據科學

  • 使用R語言進行統計分析

  • 從計劃到交付的項目生命周期

  • 多個瞬間熟悉的使用案例

  • 有效數據呈現的關鍵

關於作者

Nina Zumel和John Mount是一家位於舊金山的數據科學咨詢公司的共同創始人。他們都擁有卡內基梅隆大學的博士學位，並在win-vector.com上以統計學、概率論和計算機科學為主題撰寫博客。

目錄

第1部分 數據科學簡介

第2部分 建模方法

第3部分 交付結果


  1. 數據科學過程

  2. 將數據加載到R中

  3. 探索數據

  4. 管理數據



  5. 選擇和評估模型

  6. 記憶方法

  7. 線性和邏輯回歸

  8. 無監督方法

  9. 探索高級方法



  10. 文檔和部署

  11. 製作有效的演示文稿


類似商品