{"product_id":"mathematical-foundations-of-data-science-9783031190766","title":"Mathematical Foundations of Data Science","description":"\u003cp\u003e • Author(s): Tomas Hrycej\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Information Theory\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAlthough it is widely recognized that analyzing large volumes of data by intelligent methods may provide highly valuable insights, the practical success of data science has led to the development of a sometimes confusing variety of methods, approaches and views. \u003c\/p\u003e This practical textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTopics and features: \u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eFocuses on approaches supported by mathematical arguments, rather than sole computing experiences\u003c\/li\u003e\n\u003cli\u003eInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from them\u003c\/li\u003e\n\u003cli\u003eConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithms\u003c\/li\u003e\n\u003cli\u003eExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problem\u003c\/li\u003e\n\u003cli\u003eAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parameterization\u003c\/li\u003e\n\u003cli\u003eInvestigates the mathematical principles involved with natural language processing and computer vision\u003c\/li\u003e\n\u003cli\u003eKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire book\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e \u003c\/ul\u003e \u003cp\u003eAlthough this core textbook aims directly at students of computer science and\/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations \"beyond\" the sole computing experience.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45274215547031,"sku":"9783031190766","price":4366.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783031190766.webp?v=1769279245","url":"https:\/\/atlanticbooks.com\/products\/mathematical-foundations-of-data-science-9783031190766","provider":"Atlantic Books","version":"1.0","type":"link"}