Mathematical Underpinnings of Analytics: Theory and Applications (Hardcover)
暫譯: 分析的數學基礎:理論與應用(精裝版)
Peter Grindrod
- 出版商: Oxford University
- 出版日期: 2015-01-27
- 售價: $780
- 貴賓價: 9.8 折 $764
- 語言: 英文
- 頁數: 280
- 裝訂: Hardcover
- ISBN: 0198725094
- ISBN-13: 9780198725091
-
相關分類:
大數據 Big-data、Data Science
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商品描述
Analytics is the application of mathematical and statistical concepts to large data sets so as to distil insights that offer the owner some options for action and competitive advantage or value. This makes it the most desirable and valuable part of big data science.
Driven by the increased data capture from digital platforms, commercial fields are becoming data rich and analytics is growing in many sectors. This book presents analytics within a framework of mathematical theory and concepts building upon firm theory and foundations of probability theory, graphs and networks, random matrices, linear algebra, optimization, forecasting, discrete dynamical systems, and more.
Following on from the theoretical considerations, applications are given to data from commercially relevant interests: supermarket baskets; loyalty cards; mobile phone call records; smart meters; 'omic' data; sales promotions; social media; and microblogging.
Each chapter tackles a topic in analytics: social networks and digital marketing; forecasting; clustering and segmentation; inverse problems; Markov models of behavioural changes; multiple hypothesis testing and decision-making; and so on. Chapters start with background mathematical theory explained with a strong narrative and then give way to practical considerations and then to exemplar applications.
Exercises (and solutions), external data resources, and suggestions for project work are given. The book includes an appendix giving a crash course in Bayesian reasoning, for both ease and completeness.
Driven by the increased data capture from digital platforms, commercial fields are becoming data rich and analytics is growing in many sectors. This book presents analytics within a framework of mathematical theory and concepts building upon firm theory and foundations of probability theory, graphs and networks, random matrices, linear algebra, optimization, forecasting, discrete dynamical systems, and more.
Following on from the theoretical considerations, applications are given to data from commercially relevant interests: supermarket baskets; loyalty cards; mobile phone call records; smart meters; 'omic' data; sales promotions; social media; and microblogging.
Each chapter tackles a topic in analytics: social networks and digital marketing; forecasting; clustering and segmentation; inverse problems; Markov models of behavioural changes; multiple hypothesis testing and decision-making; and so on. Chapters start with background mathematical theory explained with a strong narrative and then give way to practical considerations and then to exemplar applications.
Exercises (and solutions), external data resources, and suggestions for project work are given. The book includes an appendix giving a crash course in Bayesian reasoning, for both ease and completeness.
商品描述(中文翻譯)
分析是將數學和統計概念應用於大型數據集,以提煉出洞見,為擁有者提供一些行動選擇和競爭優勢或價值。這使得分析成為大數據科學中最受歡迎和最有價值的部分。
隨著數字平台數據捕獲的增加,商業領域變得數據豐富,分析在許多行業中不斷增長。本書在數學理論和概念的框架內介紹分析,基於堅實的理論和概率論、圖形與網絡、隨機矩陣、線性代數、優化、預測、離散動態系統等的基礎。
在理論考量之後,應用將針對商業相關的數據進行探討:超市購物籃;忠誠卡;手機通話記錄;智能電表;'omic' 數據;銷售促銷;社交媒體;以及微博。
每一章都探討分析中的一個主題:社交網絡和數字行銷;預測;聚類和分段;反問題;行為變化的馬可夫模型;多重假設檢驗和決策制定;等等。章節以背景數學理論開始,並以強烈的敘述方式解釋,然後轉向實際考量,最後提供示範應用。
書中提供了練習題(及其解答)、外部數據資源和項目工作的建議。書中還包括附錄,提供貝葉斯推理的速成課程,以便於理解和完整性。