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oapen-20.500.12657-495052021-06-15T07:20:16Z Data Science for Economics and Finance Consoli, Sergio Reforgiato Recupero, Diego Saisana, Michaela Data Mining and Knowledge Discovery Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval IT in Business Computer and Information Systems Applications Open Access Data Mining Big Data Data Analytics Decision Support Systems Semantics and Reasoning Expert systems / knowledge-based systems Business mathematics & systems Public administration Information technology: general issues Information retrieval Data warehousing bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning bic Book Industry Communication::K Economics, finance, business & management::KJ Business & management::KJQ Business mathematics & systems bic Book Industry Communication::J Society & social sciences::JP Politics & government::JPP Public administration bic Book Industry Communication::U Computing & information technology::UN Databases::UNH Information retrieval 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. 2021-06-14T09:29:42Z 2021-06-14T09:29:42Z 2021 book ONIX_20210614_9783030668914_13 9783030668914 https://library.oapen.org/handle/20.500.12657/49505 eng application/pdf Attribution 4.0 International 9783030668914.pdf https://www.springer.com/9783662630211 Springer Nature Springer 10.1007/978-3-030-66891-4 10.1007/978-3-030-66891-4 6c6992af-b843-4f46-859c-f6e9998e40d5 3983007a-5726-4f1e-b9df-3fbc771f2916 9783030668914 Springer 355 [grantnumber unknown] European Commission European Union open access
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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.
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