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Data science in R: a case studies approach to computational reasoning and problem solving / Deborah Nolan, Duncan Temple Lang

Por: Nolan, Deborah [autor/a].
Lang, Duncan Temple [autor/a].
Tipo de material: Libro impreso(a) y electrónico Libro impreso(a) y electrónico Series Editor: Boca Raton, FL: CRC Press Taylor & Francis Group, c2015Descripción: xxiii, 515 páginas : ilustraciones, mapas ; 26 centímetros.ISBN: 1482234815; 9781482234817.Tema(s): R (Lenguaje de programación para computadora) | Métodos estadísticos | Procesamiento de datosFormatos físicos adicionales: Data science in R: a case studies approach to computational reasoning and problem solvingClasificación: 519.50285 / N6 Nota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso Nota de bibliografía: Incluye bibliografía e índice: páginas 507-513 Número de sistema: 57774Resumen:
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Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers'computational reasoning of real-world data analyses.

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Acervo General (AG) 		Acervo General 519.50285 N6 Prestado 11/08/2021 ECO030008530
Libros Biblioteca Electrónica
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Incluye bibliografía e índice: páginas 507-513

Disponible para usuarios de ECOSUR con su clave de acceso

Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers'computational reasoning of real-world data analyses. eng

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