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A fresh look at visualization from the author of VisualizeThis Whether it's statistical charts, geographic maps, or the snappygraphical statistics you see on your favorite news sites, the artof data graphics or visualization is fast becoming a movement ofits own. In Data Points: Visualization That Means Something,author Nathan Yau presents an intriguing complement to hisbestseller Visualize This, this time focusing on thegraphics side of data analysis. Using examples from art, design,business, statistics, cartography, and online media, he exploresboth standard-and not so standard-concepts and ideas aboutillustrating data. Shares intriguing ideas from Nathan Yau, author of VisualizeThis and creator of flowingdata.com, with over 66,000subscribers Focuses on visualization, data graphics that help viewers seetrends and patterns they might not otherwise see in a table Includes examples from the author's own illustrations, as wellas from professionals in statistics, art, design, business,computer science, cartography, and more Examines standard rules across all visualization applications,then explores when and where you can break those rules Create visualizations that register at all levels, with DataPoints: Visualization That Means Something.
Research was conducted using winds and temperatures measured on a 1500-ft tower at a few irregularly spaced levels. The research methodology required the construction of reasonable analytic curves of wind and temperature vs height. The curves were to be capable of integration and differentiation and were to be generated and plotted by computer without human intervention. Reasonable was subjectively defined as the curve that an individual would most probably draw by hand through the same data points. Of the several techniques tried, only an algorithm consisting of Hermite interpolation between every two successive points with artificial construction of required derivatives generated reasonable curves. Derivatives are artificially constructed by a subroutine which duplicates the constraints that an individual subconsciously employs when drawing a curve through discrete data points. The report discusses the techniques investigated, graphically demonstrates the advantages and reasonableness of this algorithm, and describes it in detail. This algorithm should be applicable for fitting a continuous curve to discrete data of any sort. (Author).
Computer science is the theory, experimentation, and engineering that form the basis for the design and use of computers. This book provides over 2,000 Exam Prep questions and answers to accompany the text Data Points; Visualization That Means ... Items include highly probable exam items: lower bound, Unit testing, identity, Pseudocode, thread, Optimization, Procedural programming, Parsing, command, Access time, Access time, Function pointer, Read-only memory, Floating point, Recursive function, and more.
Mathematics for Physical Chemistry is the ideal textbook for upper-level undergraduates or graduate students who want to sharpen their mathematics skills while they are enrolled in a physical chemistry course. Solved examples and problems, interspersed throughout the presentation and intended to be
New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

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