商品描述
Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now - at least in principle - solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of "drowning in information, but starving for knowledge" the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one's exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
商品描述(中文翻譯)
每年都見證著越來越強大的電腦、越來越快速且便宜的儲存媒介，以及更高帶寬的數據連接的發展。這使人們容易相信，只要擁有足夠的數據，我們現在 - 至少在原則上 - 可以解決我們面臨的任何問題。然而，事實並非如此。儘管大型數據庫允許我們檢索許多不同的單一信息並計算簡單的聚合，但一般模式和規律往往未被察覺。此外，正是這些模式、規律和趨勢通常是最有價值的。為了避免「淹沒在信息中卻對知識感到匱乏」的危險，數據分析這一研究領域應運而生，並開發了相當多的方法和軟體工具。然而，成功的智能數據分析項目並不僅僅依賴這些工具，而是依賴於人類直覺的智能應用與計算能力的結合、扎實的背景知識與計算機輔助建模的結合，以及批判性反思與便捷的自動模型構建的結合。《智能數據分析指南》提供了許多基本數據分析技術的實用教學方法，並解釋了這些技術如何用於解決數據分析問題。主題和特點包括：引導讀者了解數據分析的過程，遵循項目理解、數據理解、數據準備、建模以及部署和監控的相互依賴步驟；為讀者提供必要的信息，以便獲得所討論主題的實踐經驗；提供支持和證明許多數據分析方法的經典統計基礎回顧，以及統計術語的詞彙表；包括使用 R 和 KNIME 的眾多示例，以及介紹開源軟體的附錄；整合插圖和案例研究風格的示例以支持教學闡述。這本實用且系統的教科書/參考書對於研究生和高年級本科生來說是必不可少的閱讀資料，對於所有面臨數據分析問題的專業人士來說也是如此。此外，這是一本在探索後可供使用的書籍。Michael R. Berthold 博士是德國康斯坦茨大學的生物信息學和信息挖掘的 Nycomed 教授。Christian Borgelt 博士是西班牙歐洲軟計算中心智能數據分析和圖形模型研究單位的首席研究員。Frank Höppner 博士是德國奧斯特法利亞應用科技大學的信息系統教授。Frank Klawonn 博士是德國奧斯特法利亞應用科技大學計算機科學系的教授，並擔任數據分析和模式識別實驗室的負責人。他同時也是德國布倫瑞克赫爾姆霍茨感染研究中心生物信息學和統計小組的負責人。
作者簡介
Prof. Dr. Michael R. Berthold is Professor for Bioinformatics and Information Mining in the Department of Computer Science at the University of Konstanz, Germany.
Prof. Dr. Christian Borgelt is Professor for Data Science in the departments of Mathematics and Computer Sciences at the Paris Lodron University of Salzburg, Austria; he also co-authored the Springer textbook, Computational Intelligence.
Prof. Dr. Frank Höppner is Professor of Information Engineering in the Department of Computer Science at Ostfalia University of Applied Sciences, Wolfenbüttel, Germany.
Prof. Dr. Frank Klawonn is Professor for Data Analysis and Pattern Recognition at the same institution and head of the Biostatistics Group at the Helmholtz Centre for Infection Research, Braunschweig, Germany; he has authored the Springer textbook, Introduction to Computer Graphics.
Dr. Rosaria Silipo is a Principal Data Scientist and Head of Evangelism at KNIME AG, Zurich, Switzerland.
作者簡介(中文翻譯)
米哈伊爾·R·貝爾托德教授是德國康斯坦茨大學計算機科學系的生物資訊學與資訊挖掘教授。
克里斯蒂安·博爾蓋特教授是奧地利薩爾茨堡巴黎洛德隆大學數學與計算機科學系的數據科學教授；他也是Springer教科書《計算智能》的共同作者。
法蘭克·霍普納教授是德國沃爾芬比特爾奧斯特法利亞應用科學大學計算機科學系的信息工程教授。
法蘭克·克拉沃恩教授是同一機構的數據分析與模式識別教授，並且是德國布倫瑞克亥姆霍茨感染研究中心生物統計小組的負責人；他是Springer教科書《計算機圖形學導論》的作者。
羅莎莉亞·西利波博士是瑞士蘇黎世KNIME AG的首席數據科學家及傳道部門負責人。
