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 datos

Cómo el muestreo aleatorio puede reducir el sesgo y ofrecer un conjunto de datos de mayor calidad, incluso con Big Data

Cómo los principios del diseño experimental ofrecen respuestas definitivas a preguntas

Cómo utilizar la regresión para estimar resultados y detectar anomalías

Técnicas de clasificación esenciales para predecir a qué categorías pertenece un registro

Métodos estadísticos de aprendizaje automático que «aprenden» a partir de los datos

Métodos de aprendizaje no supervisados para extraer significado de datos sin etiquetar

Peter 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.

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