Data Science for Economics and Finance: Methodologies and Applications
暫譯: 經濟與金融的數據科學：方法論與應用
Consoli, Sergio, Reforgiato Recupero, Diego, Saisana, Michaela
- 出版商: Springer
- 出版日期: 2021-06-10
- 售價: $2,610
- 貴賓價: 9.5 折 $2,480
- 語言: 英文
- 頁數: 355
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3030668908
- ISBN-13: 9783030668907
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相關分類:
經濟學 Economy、Data Science
海外代購書籍(需單獨結帳)
商品描述
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.
The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.
This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
商品描述(中文翻譯)
這本開放存取的書籍涵蓋了數據科學的應用，包括先進的機器學習、大數據分析、語義網技術、自然語言處理、社交媒體分析、時間序列分析等，並應用於經濟學和金融學。此外，它展示了一些成功的先進數據科學解決方案的應用，這些解決方案用於從數據中提取新知識，以改善經濟預測模型。
本書首先介紹了數據科學技術在經濟學和金融學中的應用，接著是十三章展示特定數據科學方法論應用的成功案例，涉及與經濟分析相關的新型大數據來源和技術的特定主題（例如社交媒體和新聞）；利用監督式/非監督式（深度）機器學習的大數據模型；使用自然語言處理來建立經濟和金融指標；以及通過時間序列分析進行經濟變數的預測和即時預測。
這本書對所有參與數位和數據密集型研究的經濟學和金融學相關利益相關者都具有重要意義，幫助他們理解主要的機會和挑戰，熟悉最新的方法論發現，並學習如何使用和評估新工具和框架的性能。它主要針對利用數據科學技術的數據科學家和商業分析師，同時也將成為相關學科和課程的研究生的有用資源。總體而言，讀者將學習現代且有效的數據科學解決方案，以創造經濟和金融應用的具體創新。
作者簡介
Diego Reforgiato Recupero is an Associate Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy, where he is also a member of the Technical Commission for Patents and Spin-offs. His interests span from Semantic Web, graph theory, and smart grid optimization to sentiment analysis, data mining, big data, natural language processing, and human-robot interaction. He is the author of several research publications in peer-reviewed international journals, edited books, and leading conferences in these fields. He is Director of the Laboratory of Human Robot Interaction and Co-Director of the Laboratory of Artificial Intelligence and Big Data. He is also affiliated with the National Research Council of Italy (CNR) where he is a member of the Semantic Technology Laboratory and passionate about bringing the research output to the market.
Michaela Saisana is Head of the Monitoring, Indicators and Impact Evaluation Unit and she also leads the European Commission's Competence Centre on Composite Indicators and Scoreboards (COIN) at the Joint Research Centre in Italy. She has been working in the JRC since 1998, where she obtained a prize as "Best Young Scientist of the Year" in 2004 and together with her team the "JRC Policy Impact Award" for the Social Scoreboard of the European Pillar of Social Rights in 2018. Specializing on process optimization and spatial statistics, she is actively involved in promoting a sound development and responsible use of performance monitoring tools which feed into EU policy formulation and legislation in a wide range of fields.
作者簡介(中文翻譯)
Sergio Consoli 是歐洲委員會（聯合研究中心）的一名科學項目官員，工作地點在意大利，參與「大數據與經濟發展預測」項目，旨在探索新穎的大數據來源和方法，以提供更好的經濟預測。Sergio 曾擔任飛利浦研究數據科學部的高級科學家、意大利總理府的計算機工程官員，以及意大利國家研究委員會的初級研究員。Sergio 的教育背景和科學經驗涵蓋數據科學、運籌學、人工智慧、知識工程和機器學習等領域。他在同行評審的國際期刊上發表了多篇研究論文，擁有多項專利，編輯過書籍，並在這些領域主辦過重要會議。
Diego Reforgiato Recupero 是意大利卡利亞里大學數學與計算機科學系的副教授，同時也是專利和創業技術委員會的成員。他的研究興趣涵蓋語義網、圖論、智能電網優化、情感分析、數據挖掘、大數據、自然語言處理和人機互動等領域。他在同行評審的國際期刊上發表了多篇研究論文，編輯過書籍，並在這些領域主辦過重要會議。他是人機互動實驗室的主任，也是人工智慧與大數據實驗室的共同主任。他還與意大利國家研究委員會（CNR）有關聯，是語義技術實驗室的成員，熱衷於將研究成果推向市場。
Michaela Saisana 是監測、指標和影響評估單位的負責人，同時她還領導歐洲委員會在意大利聯合研究中心的複合指標和計分板能力中心（COIN）。自1998年以來，她一直在JRC工作，並於2004年獲得「年度最佳青年科學家」獎，與她的團隊一起於2018年獲得「JRC政策影響獎」，以表彰其對歐洲社會權利支柱社會計分板的貢獻。專注於過程優化和空間統計，她積極參與促進性能監測工具的合理發展和負責任使用，這些工具為歐盟政策制定和立法提供支持，涵蓋廣泛的領域。
