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Paperback. Zustand: Fine. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Peter Bruce; Andrew Bruce; Peter Gedeck. O'Reilly Media, 2020. 360pp. Language: English. Note:

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Paperback. Zustand: New. Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data.

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Zustand: New. Idioma/Language: Español. Los métodos estadísticos son una parte fundamental de la ciencia de datos, pero pocos científicos de datos tienen una formación avanzada en estadística. Los cursos y libros sobre estadística básica rara vez tratan el tema desde la perspectiva de la ciencia de datos. La segunda edición de este libro incluye ejemplos detallados de Python, ofrece una orientación práctica sobre la aplicación de los métodos estadísticos a la ciencia de datos, te indica cómo evitar su uso incorrecto y te aconseja sobre lo que es y lo que no es importante. Muchos recursos de la ciencia de datos incorporan métodos estadísticos, pero carecen de una perspectiva estadística más profunda. Si estás familiarizado con los lenguajes de programación R o Python y tienes algún conocimiento de estadística, este libro suple esas carencias de una forma práctica, accesible y clara. Con este libro aprenderás:Por qué el análisis exploratorio de datos es un paso preliminar clave en la ciencia de datosCómo el muestreo aleatorio puede reducir el sesgo y ofrecer un conjunto de datos de mayor calidad, incluso con Big DataCómo los principios del diseño experimental ofrecen respuestas definitivas a preguntasCómo utilizar la regresión para estimar resultados y detectar anomalíasTécnicas de clasificación esenciales para predecir a qué categorías pertenece un registroMétodos estadísticos de aprendizaje automático que «aprenden» a partir de los datosMétodos de aprendizaje no supervisados para extraer significado de datos sin etiquetarPeter Bruce es el fundador del Institute for Statistics Education en Statistics. com. Andrew Bruce es científico investigador jefe en Amazon y tiene más de 30 años de experiencia en estadística y ciencia de datos. Peter Gedeck es científico de datos senior en Collaborative Drug Discovery, desarrolla algoritmos de aprendizaje automático para pronosticar propiedades de posibles futuros fármacos. 10 *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.

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Rustica (tapa blanda). Zustand: New. Zustand des Schutzumschlags: Nuevo. 01. Los métodos estadísticos son una parte fundamental de la ciencia dedatos, pero pocos científicos de datos tienen una formación avanzadaen estadística. Los cursos y libros sobre estadística básica rara veztratan el tema desde la perspectiva de la ciencia de datos. La segunda edición de este libro incluye ejemplos detallados de Python, ofreceuna orientación práctica sobre la aplicación de los métodosestadísticos a la ciencia de datos, te indica cómo evitar su usoincorrecto y te aconseja sobre lo que es y lo que no es importante.Muchos recursos de la ciencia de datos incorporan métodosestadísticos, pero carecen de una perspectiva estadística másprofunda. Si estás familiarizado con los lenguajes de programación R o Python y tienes algún conocimiento de estadística, este libro supleesas carencias de una forma práctica, accesible y clara. Con estelibro aprenderás: Por qué el análisis exploratorio de datos es un paso preliminar clave en la ciencia de datos Cómo el muestreo aleatoriopuede reducir el sesgo y ofrecer un conjunto de datos de mayorcalidad, incluso con Big Data Cómo los principios del diseñoexperimental. LIBRO.

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Paperback. Zustand: New. Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data.

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Gr.-8°(=22,5-25cm), Broschiert. Zustand: Gut. Übersetzung der 2. Auflage. XV, 356 S., Ill. Praktische Statistik für Data Scientists. 50+ essenzielle Konzepte mit R und Python. [Practical statistics for data scientists] Deutsche Übersetzung von Marcus Fraaß. Einband leicht bestoßen. Sonst guter Gesamtzustand. Sprache: Deutsch Gewicht in Gramm: 680.

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