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[null,"1.","Record Nr.",null,"UNINA9910773610303321"]
[null,null,"Autore",null,"Parchimowicz Katarzyna"]
[null,null,"Titolo",null,"The Regulation of Megabanks : Legal frameworks of the USA and EU / / Katarzyna Parchimowicz"]
[null,null,"Pubbl/distr/stampa",null,"©2022"]
[{"$nulls":4},"Abingdon, Oxon : , : Routledge, , 2022"]
[null,null,"ISBN",null,"1-00-327688-1"]
[{"$nulls":4},"1-003-27688-1"]
[null,null,"Descrizione fisica",null,"1 online resource (256 pages)"]
[null,null,"Collana",null,"Routledge research in finance and banking law"]
[null,null,"Disciplina",null,"346.08215"]
[null,null,"Soggetti",null,"Banks and banking, International - Law and legislation"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,null,"Sommario/riassunto","\"Global systemically important banks are the largest, most complex and, in the event of their potential failure, most threatening banking institutions in the world. The global financial crisis was a turning point for global systemically important banks, many of which contributed to the outbreak and severity of this downturn. The unfolding of the global financial crisis also revealed flaws and omissions in the legal framework applying to financial entities. In the context of global systemically important banks it clearly demonstrated that the legal regimes, both in the USA and in the EU, grossly ignored the specific character of these institutions and their systemic importance, complexity and individualism. As a result of this omission, these megabanks were long treated like any other smaller banking institution. Since the financial crisis legal systems have changed a lot since then on both sides of the Atlantic, global and national lawmakers have adopted new rules applying specifically to global systemically important banks to reduce their threat to financial stability. This book explores whether the regulatory frameworks related to G-SIBs are adequately tailored to their individualism in order to prevent them from exploiting overly general rules, as they did during the global financial crisis. Analysing the specific character and individualism of G-SIBs, in relation to their history, normal function, as well as their operations during the GFC the book the transformation of banking systems and the challenges and opportunities G-SIBs face, such as Big Tech competitors, climate-"]
[{"$nulls":4},"related requirements and the COVID-19 pandemic Taking a multidisciplinary approach which combines financial aspects of operations of G- SIBs and legal analysis the book describes G-SIB-oriented legal frameworks of the EU and the USA and assesses whether G- SIB individualism is adequately reflected, assessing trends in supervisory action when it comes to discretion in the G-SIB context to contribute to the ongoing discussions about international banking law, its problems and potential remedies to such persistent flaws\"-- Provided by the publisher."]
[null,"2.","Record Nr.",null,"UNINA9910254816703321"]
[null,null,"Autore",null,"Skiena Steven S"]
[null,null,"Titolo",null,"The Data Science Design Manual / / by Steven S. Skiena"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017"]
[null,null,"ISBN",null,"3-319-55444-1"]
[null,null,"Edizione",null,"[1st ed. 2017.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 445 p. 180 illus., 137 illus. in color.)"]
[null,null,"Collana",null,"Texts in Computer Science, , 1868-095X"]
[null,null,"Disciplina",null,"519.50285"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Quantitative research"]
[{"$nulls":4},"Information visualization"]
[{"$nulls":4},"Mathematical statistics - Data processing"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Data Analysis and Big Data"]
[{"$nulls":4},"Data and Information Visualization"]
[{"$nulls":4},"Statistics and Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"What is Data Science? -- Mathematical Preliminaries -- Data Munging -- Scores and Rankings -- Statistical Analysis -- Visualizing Data -- Mathematical Models -- Linear Algebra -- Linear and Logistic Regression -- Distance and Network Methods -- Machine Learning -- Big Data: Achieving Scale."]
[null,null,null,"Sommario/riassunto","This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of "]
[{"$nulls":4},"undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)."]