Data Science (Paperback)
暫譯: 資料科學 (平裝本)
John D. Kelleher, Brendan Tierney
- 出版商: MIT
- 出版日期: 2018-04-13
- 售價: $760
- 貴賓價: 9.5 折 $722
- 語言: 英文
- 頁數: 280
- 裝訂: Paperback
- ISBN: 0262535432
- ISBN-13: 9780262535434
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相關分類:
Data Science
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相關翻譯:
人人可懂的數據科學 (簡中版)
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商品描述
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.
The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.
It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
商品描述(中文翻譯)
對於新興的資料科學領域的簡明介紹,解釋其演變、與機器學習的關係、當前的應用、資料基礎設施問題以及倫理挑戰。
資料科學的目標是通過資料分析來改善決策。如今,資料科學決定了我們在網上看到的廣告、推薦給我們的書籍和電影、哪些電子郵件被過濾到垃圾郵件夾,甚至我們為健康保險支付的費用。本書是麻省理工學院出版社《基本知識系列》中的一部,提供了對新興資料科學領域的簡明介紹,解釋其演變、當前的應用、資料基礎設施問題以及倫理挑戰。
對於組織來說,收集、儲存和處理資料從未如此容易。資料科學的使用受到大數據和社交媒體興起、高效能計算的發展,以及深度學習等強大資料分析和建模方法的出現所驅動。資料科學涵蓋了一組原則、問題定義、演算法和過程,用於從大型資料集中提取不明顯且有用的模式。它與資料挖掘和機器學習領域密切相關,但範疇更廣。本書提供了該領域的簡要歷史,介紹了基本的資料概念,並描述了資料科學專案的各個階段。它考慮了資料基礎設施以及整合來自多個來源的資料所帶來的挑戰,介紹了機器學習的基本知識,並討論了如何將機器學習專業知識與現實世界問題聯繫起來。本書還回顧了倫理和法律問題、資料規範的發展,以及保護隱私的計算方法。最後,它考慮了資料科學的未來影響,並提供了資料科學專案成功的原則。