Big Data Science in Finance examines the mathematics, theory, and practical use of Big Data techniques in finance and investing, providing in-depth instruction on how to separate signal from noise, deal with missing data values, and utilize Big Data techniques in decision-making. \n Format: Hardback\n Length: 336 pages\n Publication date: 08 April 2021\n Publisher: John Wiley & Sons Inc\n Big Data has revolutionized Wall Street, with applied mathematics and software code driving finance and investment-decision tools. This book examines the mathematics, theory, and practical use of these techniques, designed for mathematically advanced students and discerning financial practitioners. It offers in-depth instruction on separating signal from noise, dealing with missing data values, and utilizing Big Data techniques in decision-making. Key topics include data clustering, storage optimization, Big Data dynamics, Monte Carlo methods, and more. The book provides a complete account of Big Data, including proofs, step-by-step applications, and code samples. It explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), covers vital topics in a clear, straightforward manner, compares and contrasts Big Data and Small Data, and includes Cornell University-tested educational materials. This is an important, up-to-date resource for students in economics, econometrics, and finance.\n Weight: 854g\n Dimension: 262 x 187 x 24 (mm)\n ISBN-13: 9781119602989\n \n
