Big Data Science in Finance: Mathematics and Applications (1版)

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【簡介】 Explains the mathematics, theory, and methods of Big Data as applied to finance and investingData science has fundamentally changed Wall Street--applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class 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 in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. 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, step-by-step applications, and code samplesExplains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)Covers vital topics in the field in a clear, straightforward mannerCompares, contrasts, and discusses Big Data and Small DataIncludes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slidesBig Data Science in Finance: Mathematics and Applications is an important, up-to-date 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.

Chemistry: The Central Science in SI Units (15版)

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Chemistry: The Central Science 15/E 作(編/譯)者 : BROWN 出版年份 : 2022 ISBN : 9781292407616 書號 : CX0356 幾色 : 4 規格 : 平裝 發行公司 : PEARSON 英文書名中譯 : 化學 版次 : 15E 頁數 : 1320 目錄 Table of Contents: 1. Introduction: Matter,Energy, and Measurement 2. Atoms, Molecules, and Ions 3. Chemical Reactions and Reaction Stoichiometry 4. Reactions in Aqueous Solution 5. Thermochemistry  6. Electronic Structure of Atoms  7. Periodic Properties of the Elements 8. Basic Concepts of Chemical Bonding 9. Molecular Geometry and Bonding Theories  10. Gases 11. Liquidsand IntermolecularForces 12. Solidsand Modern Materials  13. Properties of Solutions 14. Chemical Kinetics  15. Chemical Equilibrium 16. Acid—Base Equilibria 17. Additional Aspects of Aqueous Equilibria  18. Chemistry of the Environment 19. Chemical Thermodynamics 20. Electrochemistry  21. NuclearChemistry 22. Chemistry of the Nonmetals 23. Transition Metals and Coordination Chemistry 24. The Chemistry of Life: Organic and Biological Chemistry

USING SCIENCE IN CYBERSECURITY

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Deploying the scientific method in cybersecurity today is a common-sense approach that is a tough topic in the field of cybersecurity. While most publications in the field emphasize that scientific principles are necessary, there are very few, if any, guides that uncover these principles. This book will give readers practical tools for cybersecurity. It examines the path of developing cybersecurity foundations while taking into account uncertain data. Extensive examples demonstrate how to deploy cybersecurity to sort our day-to-day problems. Using Science in Cybersecurity is intended for advanced undergraduate and graduate students, researchers and practitioners in the fields of cybersecurity, information security, and science of cybersecurity. Sample Chapter(s) Chapter 1: Introduction Request Inspection Copy Contents: Introduction Data in Cybersecurity In Search of Truth Desirable Study Properties Exploratory Data Analysis Sampling in Cybersecurity Designing Structured Observations Data Analysis for Cybersecurity: Goals and Pitfalls DNS Study Network Traffic Study Malware Study Human Factors Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of cybersecurity, information security, and science of cybersecurity.