Data science in R a case studies approach to computational reasoning and problem solving

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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Main Authors: Nolan, Deborah autor/a, Lang, Duncan Temple autor/a
Format: Texto biblioteca
Language:eng
Published: Boca Raton, FL CRC Press Taylor & Francis Group c201
Subjects:R (Lenguaje de programación para computadora), Métodos estadísticos, Procesamiento de datos,
Online Access:http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=974058
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region America del Norte
libraryname Sistema de Información Bibliotecario de ECOSUR (SIBE)
language eng
topic R (Lenguaje de programación para computadora)
Métodos estadísticos
Procesamiento de datos
R (Lenguaje de programación para computadora)
Métodos estadísticos
Procesamiento de datos
spellingShingle R (Lenguaje de programación para computadora)
Métodos estadísticos
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R (Lenguaje de programación para computadora)
Métodos estadísticos
Procesamiento de datos
Nolan, Deborah autor/a
Lang, Duncan Temple autor/a
Data science in R a case studies approach to computational reasoning and problem solving
description 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.
format Texto
topic_facet R (Lenguaje de programación para computadora)
Métodos estadísticos
Procesamiento de datos
author Nolan, Deborah autor/a
Lang, Duncan Temple autor/a
author_facet Nolan, Deborah autor/a
Lang, Duncan Temple autor/a
author_sort Nolan, Deborah autor/a
title Data science in R a case studies approach to computational reasoning and problem solving
title_short Data science in R a case studies approach to computational reasoning and problem solving
title_full Data science in R a case studies approach to computational reasoning and problem solving
title_fullStr Data science in R a case studies approach to computational reasoning and problem solving
title_full_unstemmed Data science in R a case studies approach to computational reasoning and problem solving
title_sort data science in r a case studies approach to computational reasoning and problem solving
publisher Boca Raton, FL CRC Press Taylor & Francis Group
publishDate c201
url http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=974058
work_keys_str_mv AT nolandeborahautora datascienceinracasestudiesapproachtocomputationalreasoningandproblemsolving
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spelling KOHA-OAI-ECOSUR:577742021-01-11T22:04:18ZData science in R a case studies approach to computational reasoning and problem solving Nolan, Deborah autor/a Lang, Duncan Temple autor/a textBoca Raton, FL CRC Press Taylor & Francis Groupc2015engEffectively 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.Incluye bibliografía e índice: páginas 507-513Effectively 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.Disponible en formato PDFSubscripción a EBSCOhostR (Lenguaje de programación para computadora)Métodos estadísticosProcesamiento de datosDisponible en líneaData science in R: a case studies approach to computational reasoning and problem solvinghttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=974058URN:ISBN:1482234815URN:ISBN:9781482234817Disponible para usuarios de ECOSUR con su clave de acceso