Practical Data Science with R, 2/e (Paperback)
暫譯: 實用數據科學與 R，第二版（平裝本）
Nina Zumel , John Mount
- 出版商: Manning
- 出版日期: 2019-12-07
- 售價: $1,970
- 貴賓價: 9.8 折 $1,931
- 語言: 英文
- 頁數: 483
- 裝訂: Paperback
- ISBN: 1617295876
- ISBN-13: 9781617295874
-
相關分類:
R 語言
-
相關翻譯:
R數據科學實戰, 2/e (Practical Data Science with R, 2/e) (簡中版)
立即出貨 (庫存=1)
買這商品的人也買了...
-
人月神話：軟體專案管理之道 (20 週年紀念版)(The Mythical Man-Month: Essays on Software Engineering, Anniversary Edition, 2/e)$480$379 -
機率論 (修訂版)(Ross: A First Course in Probability, 8/e)$780$741 -
無瑕的程式碼－敏捷軟體開發技巧守則 (Clean Code: A Handbook of Agile Software Craftsmanship)$580$452 -
$945R Packages (Paperback) -
$1,519Fundamentals of Database Systems, 7/e (IE-Paperback) -
看板實戰 : 用一張便利貼訓練出100分高效率工作團隊 (Kanban in Action)$560$476 -
精實創業：用小實驗玩出大事業 (2017書衣新版)(The Lean Startup)$380$323 -
咖啡樣$380$323 -
A Tour of C++, 2/e (Paperback)$1,580$1,501 -
Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares (Hardcover)$1,580$1,548 -
$352生成對抗網絡入門指南 (Generative adversarial Networks) -
重構｜改善既有程式的設計, 2/e (繁中平裝版)(Refactoring: Improving The Design of Existing Code, 2/e)$800$632 -
中文自然語言處理實戰：聊天機器人與深度學習整合應用$450$351 -
$709統計反思：用R和Stan例解貝葉斯方法 (Statistical Rethinking: A Bayesian Course with Examples in R and Stan) -
Introduction to Probability, 2/e (Hardcover)$1,750$1,715 -
A First Course in Probability, 10/e (GE-Paperback)$1,340$1,273 -
機器學習開發神器！Google Cloud Platform 雲端開發應用超入門$490$417 -
$352概率、決策與博弈: 基於R語言介紹 (Probability, Decisions and Games: A Gentle Introduction Using R) -
機器學習的數學基礎 : AI、深度學習打底必讀$580$458 -
Operating System Concepts, 10/e (GE-Paperback)$1,750$1,663 -
Artificial Intelligence: A Modern Approach, 4/e (IE-Paperback)$1,460$1,431 -
Data Communications and Networking with TCP/IP Protocol Suite, 6/e (IE-Paperback)$1,250$1,568 -
$2,520R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2/e (Paperback) -
Distributed Systems (Paperback)$1,700$1,615 -
Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (Hardcover)$2,160$2,117
商品描述
This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more.
Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science. You'll jump right to real-world use cases as you 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.
商品描述(中文翻譯)
這本對任何資料科學家來說都不可或缺的書籍，展示了如何將 R 程式語言和有用的統計技術應用於日常商業情境，以及如何有效地向各級觀眾呈現結果。為了滿足日益增長的機器學習和分析需求，這個新版本增添了更多的 R 工具、建模技術等。
《使用 R 的實用資料科學（第二版）》採取以實踐為導向的方法，解釋資料科學這一不斷擴展領域的基本原則。您將直接進入真實世界的案例，將 R 程式語言和統計分析技術應用於精心解釋的行銷、商業智慧和決策支持的範例中。
購買印刷版書籍可獲得 Manning Publications 提供的免費電子書，格式包括 PDF、Kindle 和 ePub。
作者簡介
Nina Zumel co-founded Win-Vector, a data science consulting firm in San Francisco. She holds a PH.D. in robotics from Carnegie Mellon and was a content developer for EMC's Data Science and Big Data Analytics Training Course. Nina also contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.
John Mount co-founded Win-Vector, a data science consulting firm in San Francisco. He has a Ph.D. in computer science from Carnegie Mellon and over 15 years of applied experience in biotech research, online advertising, price optimization and finance. He contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.
作者簡介(中文翻譯)
Nina Zumel共同創立了位於舊金山的數據科學諮詢公司 Win-Vector。她擁有卡內基梅隆大學的機器人學博士學位，並曾擔任 EMC 的數據科學與大數據分析培訓課程的內容開發者。Nina 也為 Win-Vector 博客撰寫文章，該博客涵蓋統計學、概率論、計算機科學、數學和優化等主題。
John Mount共同創立了位於舊金山的數據科學諮詢公司 Win-Vector。他擁有卡內基梅隆大學的計算機科學博士學位，並在生物技術研究、在線廣告、價格優化和金融領域擁有超過 15 年的應用經驗。他為 Win-Vector 博客撰寫文章，該博客涵蓋統計學、概率論、計算機科學、數學和優化等主題。
