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Winning with data science / Friedman, Howard Steven (2024)

Públic ISBD Títol : Winning with data science Tipus de document : text imprès Autors : Friedman, Howard Steven, Autor ; Swaminathan, Akshay, Autor Editorial : Columbia Business School Publishing Data de publicació : 2024 Nombre de pàgines : 272 Dimensions : 24 ISBN/ISSN/DL : 978-0-231-20686-0 Idioma : Anglès (eng) Matèries : Matemàtica financera Classificació : M7 Matemàtiques i mètodes quantitatius Resum : Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject. Vista prèvia a Google Books : https://www.google.es/books/edition/Winning_with_Data_Science/6H3CEAAAQBAJ?hl=es&gbpv=0 Permalink : https://bibliotecatriasfargas.cat/pmb/opac_css/index.php?lvl=notice_display&id=4 Winning with data science [text imprès] / Friedman, Howard Steven, Autor ; Swaminathan, Akshay, Autor . - [S.l.] : Columbia Business School Publishing, 2024 . - 272 ; 24. ISBN : 978-0-231-20686-0 Idioma : Anglès (eng) Matèries : Matemàtica financera Classificació : M7 Matemàtiques i mètodes quantitatius Resum : Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject. Vista prèvia a Google Books : https://www.google.es/books/edition/Winning_with_Data_Science/6H3CEAAAQBAJ?hl=es&gbpv=0 Permalink : https://bibliotecatriasfargas.cat/pmb/opac_css/index.php?lvl=notice_display&id=4

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Codi de barres	Signatura topogràfica	Tipus de document	Localització	Secció	Estat	Volum	Nota
10117084	M7 FRI	Llibre	Biblioteca IEF Ramon Trias Fargas	Biblioteca	Disponible Disponible