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Data Analytics for Management, Banking and Finance: Theories and Application (en Inglés)
Saâdaoui, Foued ; Zhao, Yichuan ; Rabbouch, Hana (Autor)
·
Springer
· Tapa Dura
Data Analytics for Management, Banking and Finance: Theories and Application (en Inglés) - Saâdaoui, Foued ; Zhao, Yichuan ; Rabbouch, Hana
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Origen: Estados Unidos
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Lunes 29 de Julio y el
Miércoles 07 de Agosto.
Lo recibirás en cualquier lugar de Ecuador entre 1 y 3 días hábiles luego del envío.
Reseña del libro "Data Analytics for Management, Banking and Finance: Theories and Application (en Inglés)"
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 innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks
