Introduction to Statistical and Machine Learning Methods for Data Science - Patetta, Mike; Pinheiro, Carlos Andre Reis
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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…mehr

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Produktbeschreibung
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.
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Autorenporträt
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 Université de Savoie, France; Dynamic Social Networks and Human Mobility at Katholieke Universiteit Leuven, Belgium; and Urban Mobility and Multi-modal Traffic at Fundação Getúlio 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.