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Big Data Science in Finance,Used
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Explains the mathematics, theory, and methods of Big Data as applied to finance and investingData science has fundamentally changed Wall Streetapplied mathematics and software code are increasingly driving finance and investmentdecision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematicallyadvanced students and discerning financial practitioners alike, this energizing book presents new, cuttingedge content based on worldclass research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data.Comprehensive in scope, this book offers indepth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decisionmaking. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, stepbystep applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell Universitytested educational materials such as lesson plans, endofchapter questions, and downloadable lecture slidesBig Data Science in Finance: Mathematics and Applications is an important, uptodate resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.
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