Big Data Analytics Predicting Real Estate Prices
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Economic Analysis of the Digital Economy
Author: Avi Goldfarb
language: en
Publisher: University of Chicago Press
Release Date: 2015-05-08
There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. "Economics of Digitization "identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. "Economics of Digitization" will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research.
Data Analytics in Finance
Data Analytics in Finance covers the methods and application of data analytics in all major areas of finance, including buy-side investments, sell-side investment banking, corporate finance, consumer finance, financial services, real estate, insurance, and commercial banking. It explains statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis. Emphasizing financial data analytics practices with a solution- oriented purpose, it is a “one-stop-shop” of all the major data analytics aspects for each major finance area. The book paints a comprehensive picture of the data analytics process including: Statistical inference of big data Financial modeling Machine learning and AI Database querying Data engineering Data visualization Risk analysis Each chapter is crafted to provide complete guidance for many subject areas including investments, fraud detection, and consumption finance. Avoiding data analytics methods widely available elsewhere, the book focuses on providing data analytics methods specifically applied to key areas of finance. Written as a roadmap for researchers, practitioners, and students to master data analytics instruments in finance, the book also provides a collection of indispensable resources for the readers’ reference. Offering the knowledge and tools necessary to thrive in a data-driven financial landscape, this book enables readers to deepen their understanding of investments, develop new approaches to risk management, and apply data analytics to finance.
Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022)
This book highlights the recent research on soft computing, pattern recognition, nature-inspired computing, and their various practical applications. It presents 69 selected papers from the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) and 19 papers from the 14th World Congress on Nature and Biologically Inspired Computing (NaBIC 2022), which was held online, from December 14 to 16, 2022. A premier conference in the field of soft computing, artificial intelligence, and machine learning applications, SoCPaR-NaBIC 2022 brought together researchers, engineers, and practitioners whose work involves intelligent systems, network security, and their applications in industry. Including contributions by authors from over 25 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering.