Gedeck, Peter,bruce, Andrew,bruce, Peter (33 results)

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.

paperback. Condition: Good. 2nd Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Paperback. Condition: Good. 2nd Edition. We only honor returns for quality issues and won't accept reasons such as 'change my mind', 'find a better price', or 'school book requirement change', etc.

Condition: New.

Paperback. Condition: Brand New. German language. 9.37x6.54x0.98 inches. In Stock.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Paperback. Condition: 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.

Condition: 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.

Condition: new. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

paperback. Condition: LikeNew. Used Like New, no missing pages, no damage to binding, may have a remainder mark.

Tapa blanda. Condition: Nuevo.

Condition: New.

Rustica (tapa blanda). Condition: New. Dust Jacket Condition: 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.

Condition: Very good.

Paperback. Condition: 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.

Condition: New. In.

Paperback. Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.

Condition: New.

Condition: New. 2nd edition NO-PA16APR2015-KAP.

Condition: New.

Paperback. Condition: Brand New. 2nd edition. 342 pages. 9.00x7.00x0.75 inches. In Stock.

Paperback. Condition: 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.

Condition: New. 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 .

Paperback. Condition: 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.

Condition: good. Fast Free Shipping â" Good condition book with a firm cover and clean, readable pages. Shows normal use, including some light wear or limited notes highlighting, yet remains a dependable copy overall. Supplemental items like CDs or access codes may not be included.