Mathematical Underpinnings of Analytics: Theory and Applications (Hardcover)
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)
分析學是將數學和統計概念應用於大型數據集,以提煉洞察力,為擁有者提供行動和競爭優勢或價值的選擇。這使得它成為大數據科學中最理想和有價值的部分。
受數字平台數據捕獲的增加驅動,商業領域正變得數據豐富,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下介紹了分析,建立在企業理論和概率論、圖形和網絡、隨機矩陣、線性代數、優化、預測、離散動態系統等基礎之上。
在理論考慮之後,應用被給予商業相關利益的數據: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;和微博等。
每個章節都涉及分析的一個主題:社交網絡和數字營銷;預測;聚類和分割;反問題;行為變化的馬爾可夫模型;多重假設檢驗和決策;等等。章節以強烈的敘述方式解釋背景數學理論,然後轉向實際考慮,最後展示示例應用。
提供練習(和解答)、外部數據資源和項目工作建議。本書還包括一個附錄,提供貝葉斯推理的速成課程,以便易於理解和完整性。