Name: Geographic Data Science With Python (en Inglés)
Price: 557.77 PEN
Availability: InStock
Author: Sergio Rey|Dani Arribas-Bel (University Of Liverpool, Merseyside, United Kingdom)|Levi John Wolf (School Of Geographical Sciences)
ISBN: 9780367263119

portada Geographic Data Science With Python (en Inglés)

Formato

Libro Físico

Editorial

Crc Press,

Autor

Sergio Rey|Dani Arribas-Bel (University Of Liverpool, Merseyside, United Kingdom)|Levi John Wolf (School Of Geographical Sciences)

Idioma

Inglés

Encuadernación

Tapa Dura

ISBN13

9780367263119

Geographic Data Science With Python (en Inglés)

Sergio Rey|Dani Arribas-Bel (University Of Liverpool, Merseyside, United Kingdom)|Levi John Wolf (School Of Geographical Sciences) (Autor) · Crc Press, · Tapa Dura

Libro Nuevo

S/ 557,77

S/ 929,61

Ahorras: S/ 371,84

40% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Jueves 18 de Julio y el Martes 30 de Julio.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Geographic Data Science With Python (en Inglés)"

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. Key Features: ● Showcases the excellent data science environment in Python. ● Provides examples for readers to replicate, adapt, extend, and improve. ● Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.