Statistical Data Analytics (Hardcvoer)

<內容簡介>

  Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques.

  Provides informative, technical details for the highlighted methods.

  Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book.

  Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas.

This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.

A comprehensive introduction to statistical methods for data mining and knowledge discovery.

Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced.

Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

為了多益考高分，狂背單字、死背句型，
卻一考完就忘光光？
本書教你將苦學多時的多益，落實在不同的職場階段，
讓你隨時對照應用、輕鬆應對。

善用所學，才增強職場英語實戰力！

◎套入5大職場情境，強化英文實際運用，邊做邊學不會忘。
◎矯正職場菜英文，提升個人英文魅力。
◎補充現在多益熱門單字與文法，讓你在工作中還能學習新知識。
◎依照職務工作事項分類，可做為英文工具書，工作中現學現賣。
◎增加補充時事應用，一字多用，同時間思考多種可能。

本書以五大職場必經階段來劃分篇章：菜鳥面試、便利貼職員、獨當一面、小主管、大主管。每個階段都有精彩、仿真情境的案例，提供有機會使用到的單字、文法、片語、以及情境對話，，讓你邊看邊拾回多益能力，扎扎實實地用用在工作中的各個面向。
更重要的是，最後還有搭配多益試題，讓你學會、還能應用，自然而然將多益的聽、說、讀、寫帶入職場中。

◎本書特色
1. 多益╳職場通
台灣考生總是狂背多益單字、死背句型，考完就忘光光！本書提供實際情境案例，讓想學好商用英文的人能夠將職場英文落實應用。
2. 一本好的英文工具書
本書內容完全針對職場情境量身打造，諸如英文信件回覆、接聽電話、洽談合約……等等，讓你隨時都能對照應用、輕鬆應對。<章節目錄>

Part I Background: Introductory Statistical Analytics
1 Data analytics and data mining
2 Basic probability and statistical distributions
3 Data manipulation
4 Data visualization and statistical graphics
5 Statistical inference
Part II Statistical Learning and Data Analytics
6 Techniques for supervised learning: simple linear regression
7 Techniques for supervised learning: multiple linear regression
8 Supervised learning: generalized linear models
9 Supervised learning: classification
10 Techniques for unsupervised learning: dimension reduction
11 Techniques for unsupervised learning: clustering and association
A Matrix manipulation
B Brief introduction to R

內容簡介：

本書著重介紹在資料挖掘和統計資訊學中關鍵的方法。以初級水平清晰地描述這些方法，並擴展到選定的中級和高級技術。

本書提供了突出方法的詳細技術資訊。

本書使用開源的 R 語言作為計算工具，並展示了該語言日益增長的線上套件集合，以說明書中包含的許多分析方法。

每章結束時，提供了一系列有趣且具有挑戰性的家庭作業練習，使用了來自各種資訊應用領域的實際數據。

本書適合作為中高級本科生的課堂教材或培訓教材，以及具有足夠微積分和矩陣代數背景的研究生初學者。對於經常將統計學習應用於現代數據的從業人員，本書也可作為統計資訊學和數據分析基礎的參考書。

這本書是一本關於資料挖掘和知識發現的統計方法的全面介紹。