Practical Data Science with R (Paperback)
暫譯: 實用數據科學與 R

Nina Zumel, John Mount

Practical Data Science with R (Paperback)

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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**

《實用數據科學與 R》名副其實。它在沒有繁瑣理論的情況下解釋基本原則，直接進入您在收集、整理和分析對業務成功至關重要的數據時所面臨的實際案例。您將應用 R 程式語言和統計分析技術，仔細解釋基於行銷、商業智慧和決策支持的範例。

購買印刷書籍可獲得 Manning Publications 提供的免費 PDF、Kindle 和 ePub 格式電子書。

**關於本書**

商業分析師和開發人員越來越多地收集、整理、分析和報告關鍵商業數據。R 語言及其相關工具提供了一種簡單的方法來處理日常數據科學任務，而不需要大量的學術理論或高級數學。

《實用數據科學與 R》向您展示如何將 R 程式語言和有用的統計技術應用於日常商業情境。通過行銷、商業智慧和決策支持的範例，它展示了如何設計實驗（例如 A/B 測試）、建立預測模型，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。假設讀者對基本統計、R 或其他腳本語言有一定的了解。

**內容概覽**

- 商業專業人士的數據科學
- 使用 R 語言的統計分析
- 專案生命週期，從規劃到交付
- 許多立即熟悉的使用案例
- 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諮詢公司的共同創辦人。兩人均持有卡內基梅隆大學的博士學位，並在 win-vector.com 上撰寫有關統計、概率和計算機科學的博客。

**目錄**

**第一部分 數據科學導論**

**第二部分 建模方法**

**第三部分 結果交付**

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. 產生有效的演示文稿

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