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Características del producto

Características principales

Título del libro
Introduction To Statistical And Machine Learning Methods Fo
Autor
Pinheiro, Dr. Carlos Andre Reis Andre Reis
Idioma
Inglés
Editorial del libro
SAS Institute
Es kit
No
Tapa del libro
Blanda
Volumen del libro
1
Año de publicación
2021
Marca
SAS Institute
Modelo
Ingles

Otros

Cantidad de páginas
170
Con páginas para colorear
No
Con realidad aumentada
No
Género del libro
Informática y tecnología
Subgéneros del libro
Tecnología y ingeniería
Tipo de narración
Novela
Con cierre
No
Edad mínima recomendada
0 años
Escrito en imprenta mayúscula
No
ISBN
9781953329608

Descripción

- ANTES DE COMPRAR PREGUNTE FECHA DE ENTREGA.
- ENVIAMOS POR MERCADOENVIOS
- PUEDE RETIRAR POR AHORA SOLO POR QUILMES, MICROCENTRO ESTA CERRADO, POR ESO...
- EN CABA (CAPITAL FEDERAL) ENVIAMOS SIN CARGO ESTE PRODUCTO.
- FORMA DE PAGO : MERCADOPAGO
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- SOMOS IMPORTADORES DIRECTOS, ESTE PRODUCTO SE COMPRA Y SE IMPORTA DESDE ESTADOS UNIDOS, ESTO IMPLICA QUE USTED ESTA COMPRANDO EL MISMO PRODUCTO QUE COMPRARÍA UN CLIENTE DE ESE PAÍS.

- ANTES DE REALIZAR UNA CONSULTA, VISUALICE TODAS LAS IMAGENES DEL PRODUCTO.
Descripción provista por la editorial :

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have. About the Author Dr. Carlos Andre Reis Pinheiro is a Principal Data Scientist at SAS and a Visiting Professor at Data ScienceTech Institute in France. He has been working in analytics since 1996 for some of the largest telecommunications providers in Brazil in multiple roles from technical to executive. He worked as a Senior Data Scientist for EMC in Brazil on network analytics, optimization, and text analytics projects, and as a Lead Data Scientist for Teradata on machine learning projects. Dr. Pinheiro has a BSc in Applied Mathematics and Computer Science, an MSc in Computing, and a DSc in Engineering from the Federal University of Rio de Janeiro. Carlos has completed a series of postdoctoral research terms in different fields, including Dynamic Systems at IMPA, Brazil; Social Network Analysis at Dublin City University, Ireland; Transportation Systems at Universite de Savoie, France; Dynamic Social Networks and Human Mobility at Katholieke Universiteit Leuven, Belgium; and Urban Mobility and Multi-modal Traffic at Fundaçao Getulio Vargas, Brazil. He has published several papers in international journals and conferences, and he is author of Social Network Analysis in Telecommunications and Heuristics in Analytics: A Practical Perspective of What Influence Our Analytical World, both published by John Wiley and Sons, Inc.Michael Patetta has been a statistical instructor for SAS since 1994. He teaches a variety of courses including Supervised Machine Learning Procedures Using SAS Viya in SAS Studio, Predictive Modeling Using Logistic Regression, Introduction to Data Science Statistical Methods, and Regression Methods Using SAS Viya. Before coming to SAS, Michael worked in the North Carolina State Health Department for 10 years as a health statistician and program manager. He has authored or co-authored 10 published papers since 1983. Michael has a BA from the University of Notre Dame and a MA from the University of North Carolina at Chapel Hill. In his spare time, he loves to hike in National Parks.
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