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Titlebook: Data Analytics for Management, Banking and Finance; Theories and Applica Foued Saadaoui,Yichuan Zhao,Hana Rabbouch Book 2023 The Editor(s)

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發(fā)表于 2025-3-21 16:31:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Analytics for Management, Banking and Finance
副標(biāo)題Theories and Applica
編輯Foued Saadaoui,Yichuan Zhao,Hana Rabbouch
視頻videohttp://file.papertrans.cn/263/262699/262699.mp4
概述Includes big data sources and applications on time series analysis, forecasting and more.Presents the main concepts and a practical guidance on the use of data analytics and visualization techniques.F
圖書封面Titlebook: Data Analytics for Management, Banking and Finance; Theories and Applica Foued Saadaoui,Yichuan Zhao,Hana Rabbouch Book 2023 The Editor(s)
描述.This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance..It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies..The book is for stakeholders involved in research and
出版日期Book 2023
關(guān)鍵詞Big Data; Banking; Financial Data Mining; Data Analytics; Finance; Machine Learning; Fintech Industry; Supp
版次1
doihttps://doi.org/10.1007/978-3-031-36570-6
isbn_softcover978-3-031-36572-0
isbn_ebook978-3-031-36570-6
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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978-3-031-36572-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Mauro Cappelletti,Joseph M. Perilloeria. Meanwhile, investors lend money to certain borrowers and possibly earn the profit generated by the interest rate. Compared with the traditional banking system, P2P lending has a lower operating cost and a faster approval process, which makes it a major competitor to the traditional banking sys
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Katarína Staroňová,Gyorgy Gajduschekancial institutions to make better-informed decisions in an increasingly complicated and competitive industry. Machine learning models have recently advanced and been integrated into data analytics, allowing researchers and practitioners to make sense of large and complex data and extract relevant i
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Civil Servants and Politics in Germanyata contains valuable or interesting information not easy or nearly impossible to get manually or using traditional approaches. This key information drives the business, marketing, and financial services very effectively, more economically, and with high growth in less time. This can be achieved thr
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https://doi.org/10.1007/978-3-031-40150-3 stationary wavelet transform (SWT), and empirical mode decomposition (EMD), for analyzing financial time series data in Matlab. These techniques allow for the decomposition of financial time series data into different frequency bands and the identification of trends and patterns at different scales
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