Large-Scale Data Analytics with Python and Spark: A Hands-On Guide to Implementing Machine Learning Solutions (Paperback)

Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.

基於作者們豐富的教學經驗，這本實踐性的研究生教科書教授如何進行大規模數據分析並設計應用於大數據的機器學習解決方案。著重於基礎知識，這本經過廣泛班級測試的教科書引導學生掌握處理大規模數據的關鍵原則和範式，介紹大規模數據分析框架（Hadoop、Spark），並解釋如何實施機器學習以利用大數據。它獨特之處在於涵蓋了有志成為數據科學家所需了解的原則，而不會過於細節而令人困惑。真實世界的例子、實踐編碼練習和實驗室結合了非常清晰的解釋，以最大程度地提高學生的參與度。明確的學習目標、提供給教師的線上解答練習、講義幻燈片以及一套越來越難的Jupyter Notebooks實驗室練習，提供了一個有條理且方便的教學套件。這是一個理想的教學資源，適用於計算機/數據科學系的大規模數據分析與機器學習課程。